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[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
Software and model selection challenges inmeta-analysis
MetaEasy, model assumptions, homogeneity and IPD
Evangelos Kontopantelis
Centre for Health InformaticsInstitute of Population Health
University of Manchester
Attikon HospitalAthens, 2 June 2014
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
Outline
1 Meta-analysis overview
2 Two-stage meta-analysis
3 One-stage meta-analysis
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
The heterogeneity issue
Outline
1 Meta-analysis overview
2 Two-stage meta-analysis
3 One-stage meta-analysis
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Add key point.]
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[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
The heterogeneity issue
Timeline
Efforts to pool results from individual studies back as far as1904The first attempt that assessed a therapeutic interventionwas published in 1955In 1976 Glass first used the term to describe a "statisticalanalysis of a large collection of analysis results fromindividual studies for the purpose of integrating thefindings"Relatively young and dynamic field of research, reflectingimportance of MA and potential for conclusive answers
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
The heterogeneity issue
Meta-analysing reported study results
A two-stage processthe relevant summary effect statistics are extracted frompublished papers on the included studiesthese are then combined into an overall effect estimateusing a suitable meta-analysis model
However, problems often arisepapers do not report all the statistical information requiredas inputpapers report a statistic other than the effect size whichneeds to be transformed with a loss of precisiona study might be too different to be included (populationclinically heterogeneous)
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
The heterogeneity issue
Individual Patient DataIPD
These problems can be avoided when IPD from eachstudy are available
outcomes can be easily standardisedclinical heterogeneity can be addressed with subgroupanalyses and patient-level covariate controlling
Can be analysed in a single- or two-stage processmixed-effects regression models can be used to combineinformation across studies in a single stagethis is currently the best approach, with the two-stagemethod being at best equivalent in certain scenarios
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
The heterogeneity issue
Meta-analyses on the rise
Meta-analysis studiesare used more and moresince they seem to be auseful ‘summary’ tool.However critics arguethat one cannot combinestudies when they aretoo diverse(heterogeneous).
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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[Add key point.]
[Sub-bullet] [Sub-bullet]
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[Sub-bullet] [Sub-bullet]
RESULTS
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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
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[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
The heterogeneity issue
Outline
1 Meta-analysis overviewThe heterogeneity issue
2 Two-stage meta-analysis
3 One-stage meta-analysis
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
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RESULTS
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
The heterogeneity issue
When Robin ruins the party...
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
RESULTS
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
The heterogeneity issue
Heterogeneitytread lightly!
When the effect of the intervention varies significantly fromone study to anotherIt can be attributed to clinical and/or methodologicaldiversity
Clinical: variability that arises from different populations,interventions, outcomes and follow-up timesMethodological: relates to differences in trial design andquality
Detecting quantifying and dealing with heterogeneity canbe very hard
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
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[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
The heterogeneity issue
Absence of heterogeneity
Assumes that thetrue effects of thestudies are allequal anddeviations occurbecause ofimprecision ofresultsAnalysed with thefixed-effectsmethod
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
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[Graphic title]
RESULTS
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
The heterogeneity issue
Presence of heterogeneity
Assumes thatthere is variation inthe size of the trueeffect amongstudies (in additionto the imprecisionof results)Analysed withrandom-effectsmethods
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
The heterogeneity issue
Random or fixed?two ‘schools’ of thought
Fixed-effect (FE)‘what is the average result of trials conducted to date’?assumption-free
Random-effects (RE)‘what is the true treatment effect’?various assumptions
normally distributed trial effectsvarying treatment effect across populations although findingslimited since based on observed studies only
more conservative; findings potentially more generalisable
Researchers reassured when τ2 = 0FE often used when low heterogeneity detected
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
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[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
The heterogeneity issue
Not simple
Start(sort of)
Outcome(s) continuous
Inverse Variance weighting methods (IV)
Yes
Fixed-effect by conviction
Fixed-effect IV model
Yes
No
Detected heterogeneity
No Random-effects IV model
DL VC ML
REMLPL
Yes
Outcome(s) dichotomousNo
Maentel-Haenszel methods (MH)
Fixed-effect by conviction
Fixed-effect MH true model
YesDetected
heterogeneity
NoCombining dichotomous
and continuous outcomes
Transform dichotomous
outsomes to SMD
Feeling adventurous?
Yes
Yes! No!Rare events
Very rare events?
Estimate heterogeneity (τ2)No
No
Random-effects MH-IV hybrid model
Yes
Peto methods (P)
Fixed-effect Peto true model
YesNo
Outcome(s) time-to-eventNo
Fixed-effect Peto O-E true model
Yes
Bayesian?
No
τ2 est
BP
MVaMVb
Yes
Random-effects IV model
DL
τ2 estimation
DL DL2 DLb VC
VC2ML REML PL
Non-zero prior
Yes
τ2 est
B0
No
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Outline
1 Meta-analysis overview
2 Two-stage meta-analysis
3 One-stage meta-analysis
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
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cien
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y Ca
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rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Challenges with two-stage meta-analysis
Heterogeneity is common and the fixed-effect model isunder fireMethods are asymptotic: accuracy improves as studiesincrease. But what if we only have a handful, as is usuallythe case?Almost all random-effects models (except ProfileLikelihood) do not take into account the uncertainty in τ2.Is this, practically, a problem?DerSimonian-Laird is the most common method ofanalysis, since it is easy to implement and widely available,but is it the best?
Kontopantelis Software and model selection challenges in meta-analysis
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Grou
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[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Challenges with two-stage meta-analysis...continued
Can be difficult to organise since...outcomes likely to have been disseminated using a varietyof statistical parametersappropriate transformations to a common format requiredtedious task, requiring at least some statistical adeptness
Parametric random-effects models assume that both theeffects and errors are normally distributed. Are methodsrobust?Sometimes heterogeneity is estimated to be zero,especially when the number of studies is small. Goodnews?
Kontopantelis Software and model selection challenges in meta-analysis
Heal
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p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Outline
1 Meta-analysis overview
2 Two-stage meta-analysisthe MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
3 One-stage meta-analysis
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Organising
Data initially collected using data extraction formsA spreadsheet is the next logical step to summarise thereported study outcomes and identify missing dataSince in most cases MS Excel will be used we developedan add-in that can help with most processes involved inmeta-analysisMore useful when the need to combine differently reportedoutcomes arises
Kontopantelis Software and model selection challenges in meta-analysis
Heal
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rimar
y Ca
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esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
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[Graphic title]
RESULTS
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
What it can do
Help with the data collection using pre-formattedworksheets.Its unique feature, which can be supplementary to othermeta-analysis software, is implementation of methods forcalculating effect sizes (& SEs) from different input typesFor each outcome of each study...
it identifies which methods can be usedcalculates an effect size and its standard errorselects the most precise method for each outcome
Kontopantelis Software and model selection challenges in meta-analysis
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Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
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[Graphic title]
RESULTS
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
What it can do...continued
Creates a forest plot that summarises all the outcomes,organised by studyUses a variety of standard and advanced meta-analysismethods to calculate an overall effect
a variety of options is available for selecting whichoutcome(s) are to be meta-analysed from each study
Plots the results in a second forest plotReports a variety of heterogeneity measures, includingCochran’s Q, I2, HM
2 and τ2 (and its estimated confidenceinterval under the Profile Likelihood method)
Kontopantelis Software and model selection challenges in meta-analysis
Heal
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y Ca
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rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
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[Graphic title]
RESULTS
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Advantages
Free (provided Microsoft Excel is available)Easy to use and time savingExtracted data from each study are easily accessible, canbe quickly edited or corrected and analysis repeatedChoice of many meta-analysis models, including someadvanced methods not currently available in other softwarepackages (e.g. Permutations, Profile Likelihood, REML)Unique forest plot that allows multiple outcomes per studyEffect sizes and standard errors can be exported for use inother meta-analysis software packages
Kontopantelis Software and model selection challenges in meta-analysis
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cien
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rimar
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[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
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RESULTS
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CONCLUSIONS
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RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Installing
Latest version available from www.statanalysis.co.ukCompatible with Excel 2003, 2007 and 2010Manual provided but also described in:
Kontopantelis E and Reeves DMetaEasy: A Meta-Analysis Add-In for Microsoft ExcelJournal of Statistical Software, 30(7):1-25, 2009
Play video clip
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
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RESULTS
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CONCLUSIONS
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RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Outline
1 Meta-analysis overview
2 Two-stage meta-analysisthe MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
3 One-stage meta-analysis
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Stata implementation
MetaEasy methods implemented in Stata under:metaeff, which uses the different study input to provideeffect sizes and SEsmetaan, which meta-analyses the study effects with afixed-effect or one of five available random-effects models
To install, type in Stata:ssc install <command name>help <command name>
Described in:Kontopantelis E and Reeves Dmetaan: Random-effects meta-analysisThe Stata Journal, 10(3):395-407, 2010
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Outline
1 Meta-analysis overview
2 Two-stage meta-analysisthe MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
3 One-stage meta-analysis
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
RESULTS
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Many random-effects methodswhich to use?
DerSimonian-Laird (DL): Moment-based estimator of bothwithin and between-study varianceMaximum Likelihood (ML): Improves the variance estimateusing iterationRestricted Maximum Likelihood (REML): an ML variationthat uses a likelihood function calculated from atransformed set of dataProfile Likelihood (PL): A more advanced version of MLthat uses nested iterations for convergingPermutations method (PE): Simulates the distribution ofthe overall effect using the observed data
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
RESULTS
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Performance evaluationour approach
Simulated various distributions for the trueeffects:
NormalSkew-NormalUniformBimodal
Created datasets of 10,000meta-analyses for various numbers ofstudies and different degrees ofheterogeneity, for each distribution
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Performance evaluationour approach
Compared all methods in terms of:Coverage, the rate of true negatives when the overall trueeffect is zeroPower, the rate of true positives when the true overall effectis non-zero
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Coverage by methodLarge heterogeneity across various between-study variance distributions
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
%
2 5 8 11 14 17 20 23 26 29 32 35number of studies
Zero variance Normal Skew−normal
Bimodal Uniform
H=2.78 − I=64%FE − Coverage
0.80
0.85
0.90
0.95
1.00
%
2 5 8 11 14 17 20 23 26 29 32 35number of studies
Zero variance Normal Skew−normal
Bimodal Uniform
H=2.78 − I=64%DL − Coverage
0.70
0.75
0.80
0.85
0.90
0.95
1.00
%2 5 8 11 14 17 20 23 26 29 32 35
number of studies
Zero variance Normal Skew−normal
Bimodal Uniform
H=2.78 − I=64%ML − Coverage
0.80
0.85
0.90
0.95
1.00
%
2 5 8 11 14 17 20 23 26 29 32 35number of studies
Zero variance Normal Skew−normal
Bimodal Uniform
H=2.78 − I=64%REML − Coverage
0.80
0.85
0.90
0.95
1.00
%
2 5 8 11 14 17 20 23 26 29 32 35number of studies
Zero variance Normal Skew−normal
Bimodal Uniform
H=2.78 − I=64%PL − Coverage
0.90
0.95
1.00
%
2 5 8 11 14 17 20 23 26 29 32 35number of studies
Zero variance Normal Skew−normal
Bimodal Uniform
H=2.78 − I=64%PE − Coverage
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
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Grou
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[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Power by methodLarge heterogeneity across various between-study variance distributions
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
%
2 5 8 11 14 17 20 23 26 29 32 35number of studies
Zero variance Normal Skew−normal
Bimodal Uniform
H=2.78 − I=64%FE − Power (25th centile)
0.10
0.20
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0.90
1.00
%
2 5 8 11 14 17 20 23 26 29 32 35number of studies
Zero variance Normal Skew−normal
Bimodal Uniform
H=2.78 − I=64%DL − Power (25th centile)
0.20
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0.90
1.00
%2 5 8 11 14 17 20 23 26 29 32 35
number of studies
Zero variance Normal Skew−normal
Bimodal Uniform
H=2.78 − I=64%ML − Power (25th centile)
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
%
2 5 8 11 14 17 20 23 26 29 32 35number of studies
Zero variance Normal Skew−normal
Bimodal Uniform
H=2.78 − I=64%REML − Power (25th centile)
0.00
0.10
0.20
0.30
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0.50
0.60
0.70
0.80
0.90
1.00
%
2 5 8 11 14 17 20 23 26 29 32 35number of studies
Zero variance Normal Skew−normal
Bimodal Uniform
H=2.78 − I=64%PL − Power (25th centile)
0.10
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0.80
0.90
1.00
%
2 5 8 11 14 17 20 23 26 29 32 35number of studies
Zero variance Normal Skew−normal
Bimodal Uniform
H=2.78 − I=64%PE − Power (25th centile)
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Which method then?
Within any method, consistent results across alldistribution typesRobust against even severe violations of normalityPL has a ‘reasonable’ coverage in most situations,especially for moderate and large heterogeneiryREML and DL perform similarly and better than PL onlywhen heterogeneity is low (I2 < 15%)Described in:
Kontopantelis E and Reeves DPerformance of statistical methods for meta-analysis whentrue study effects are non-normally distributedStat Methods Med Res, published online Dec 9 2010
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
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esea
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Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Outline
1 Meta-analysis overview
2 Two-stage meta-analysisthe MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
3 One-stage meta-analysis
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Cochrane Database for Systematic Reviews
Richest resource of meta-analyses in the worldFifty-four active groups responsible for organising, advisingon and publishing systematic reviewsAuthors obliged to use RevMan and submit the data andanalyses file along with the review, contributing to thecreation of a vast data resourceRevMan offers quite a few fixed-effect choices but only theDerSimonian-Laird random-effects method has beenimplemented to quantify and account for heterogeneity
hidden data
Kontopantelis Software and model selection challenges in meta-analysis
Heal
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y Ca
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Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
‘Real’ DataCochrane Database for Systematic Reviews
Python code to crawl Wiley website for RevMan filesDownloaded 3,845 relevant RevMan files (of 3,984available in Aug 2012) and imported in StataEach file a systematic reviewWithin each file, various research questions might havebeen posed
investigated across various relevant outcomes?variability in intervention or outcome?
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
‘Real’ DataCochrane Database for Systematic Reviews
Cochrane database
CD000006
Group: Pregnancy and Childbirth
Review name: Absorbable suture
materials for primary repair of episiotomy and
second degree tears
Meta-analysis 1Synthetic sutures
versus catgut
Meta-analysis 2Fast-absorbing synthetic versus
standard absorbable synthetic material
Meta-analysis 3Glycerol impregnated catgut (softgut)
versus chromic catgut
Meta-analysis 4Monofilament versus standard
polyglycolic sutures
Outcome 1.1Short-term pain: pain at day 3 or less
(women experiencing any pain)
Subgroup 1.1.1Standard synthetic; k=9
Subgroup 1.1.2Fast absorbing; k=1
Outcome 1.9Dyspareunia - at 3 months
postpartumSubgroup 1.9.1
Standard synthetic; k=5
Subgroup 1.9.2Fast absorbing; k=1
Main 1.9.0k=6
Main 1.1.0k=10
Outcome 2.1Short-term pain: at 3 days or less
Main 2.1.0k=3
Outcome 2.11Maternal satisfaction: satisfied with
repair at 12 months
Main 2.11.0k=1
Outcome 3.1Short-term pain: pain at 3 days or
less
Main 3.1.0k=1
Outcome 3.8Dyspareunia at 6 - 12 months
Main 3.8.0k=1
Outcome 4.1Short-term pain: mean pain scores at
3 days
Main 4.1.0k=1
Outcome 4.4Wound problems at 8 - 12 weeks:
women seeking professional help for problem with perineal repair
Main 4.4.0k=1
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
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Grou
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[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
The questions
Investigate the potential bias when assuming homogeneityAssess method performance in relation to heterogeneityestimates in simulationsExamine the distribution of measured heterogeneity in allCochrane meta-analysesPresent details on the number of meta-analysed studiesand model selectionAssess the sensitivity of results and conclusions usingalternative models
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Which method?simulation results
Bayesian methods (assuming a prior heterogeneity value)did very well for very small meta-analysesOur suggested variant of the non-parametric bootstrap forthe standard estimate, DLb, seemed best method overall
especially in detecting heterogeneitywhich appears to be a big problem: DL failed to detect highτ2 for over 50% of small meta-analyses
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
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[Add key point.]
[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Meta-analyses numbers
Of the 3,845 files 2,801 had identified relevant studies andcontained any data98,615 analyses extracted 57,397 of which meta-analyses
32,005 were overall meta-analyses25,392 were subgroup meta-analyses
Estimation of an overall effectPeto method in 4,340 (7.6%)Mantel-Haenszel in 33,184 (57.8%)Inverse variance in 19,873 (34.6%)random-effects more prevalent in inverse variance methodsand larger meta-analyses
34% of meta-analyses on 2 studies (53% k ≤ 3)!
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Meta-analyses by Cochrane group
22
Figures Figure 1: All meta-analyses, including single-study and subgroup meta-analyses
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Single Study Fixed-effect model (by choice or necessity) Random-effects model
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
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RESULTS
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CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Meta-analyses by method choice
23
Figure 2: Model selection by number of available studies (and % of random-effects meta-analyses)*
*note that in many cases fixed-effect models were used when heterogeneity was detected
Figure 3: Comparison of zero between-study variance estimates rates in the Cochrane library data and in simulations, using the DerSimonian-Laird method*
*Normal distribution of the effects assumed in the simulations (more extreme distributions produced similar results).
21%
27%
31% 37%
41% 51%
15%
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22%
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Observed
true τ^2=0.01
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Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
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Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
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[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Comparing Cochrane data with simulated
To assess the validity of a homogeneity assumption wecompared the percentage of zero estimates, in real andsimulated dataCalculated DL heterogeneity estimate for all Cochranemeta-analysesPercentage of zero estimates equivalent to moderate-highheterogeneity simulated dataSuggests that mean true heterogeneity is higher thangenerally assumed but fails to be detected; especially forsmall meta-analyses
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
0% 20% 40% 60% 80% 100%
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
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[Add key point.]
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[Add key point.]
[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Comparing Cochrane data with simulated
23
Figure 2: Model selection by number of available studies (and % of random-effects meta-analyses)*
*note that in many case fixed-effect models were used when heterogeneity was detected
Figure 3: Comparison of zero between-study variance estimates rates in the Cochrane library data and in simulations, using the DerSimonian-Laird method*
*Normal distribution of the effects assumed in the simulations (more extreme distributions produced similar results).
21%
27%
31% 37%
41% 51%
15%
19%
22%
22%
27% 30%
0
2000
4000
6000
8000
10000
12000
2 3 4 5 6-9 10+Number of Studies in meta-analysis
Peto (FE) Inverse Variance (FE) Inverse Variance (RE) Mantel-Haenszel (FE) Mantel-Haenszel (RE)
0
10
20
30
40
50
60
70
80
90
100
2 3 4 5 10 20
% o
f zer
o τ^
2 es
timat
es w
ith D
erSi
mon
ian-
Laird
Number of studies in meta-analyis
Observed
true I^2=15%
true I^2=35%
true I^2=64%
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
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rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.]
[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
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[Add key point.]
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[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Reanalysing the Cochrane data
We applied all methods to all 57,397 meta-analyses toassess heterogeneity distributions and the sensitivity of theresults and conclusionsFor simplicity discuss differences between standardmethods and DLb; not a perfect method but one thatperformed well overallAs in simulations, DLb identifies more heterogeneousmeta-analyses; τ2
DL = 0 for 50.5% & τ2DLb = 0 for 31.2%
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
0% 20% 40% 60% 80% 100%
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
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[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Distributions for τ2
050
010
0015
0020
00
# of
met
a-an
alys
es
0 .1 .2 .3 .4 .5
t2 estimate
Zero est(%): DL=44.9, DLb=29.6, VC=48.9 REML=45.4 ML=62.2, B0=49.2, VC2=44.3, DL2=45.3Non-convergence(%): ML=0.7, REML=1.4.
Inverse Variance
010
0020
0030
0040
0050
00
# of
met
a-an
alys
es
0 .1 .2 .3 .4 .5
t2 estimate
Zero est(%): DL=54.2, DLb=32.7, VC=58.8 REML=55.6 ML=75.0, B0=59.6, VC2=53.9, DL2=55.5Non-convergence(%): ML=1.3, REML=1.9.
Mantel-Haenszel
020
040
060
0
# of
met
a-an
alys
es
0 .1 .2 .3 .4 .5
t2 estimate
Zero est(%): DL=50.8, DLb=27.3, VC=54.2 REML=51.4 ML=70.0, B0=54.8, VC2=49.6, DL2=51.0Non-convergence(%): ML=0.6, REML=1.0.
Peto & O-E
020
0040
0060
0080
00
# of
met
a-an
alys
es
0 .1 .2 .3 .4 .5
t2 estimate
Zero est(%): DL=50.7, DLb=31.2, VC=55.0 REML=51.7 ML=70.2, B0=55.6, VC2=50.2, DL2=51.6Non-convergence(%): ML=1.0, REML=1.6.
all methods
non-zero estimates only
DL DLb VC ML
REML B0 VC2 DL2
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
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[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
Changes in results and conclusionse.g. inverse variance analyses
RevMan DerSimonian-Laird Random-effects method says
heterogeneity is present
Analysis with bootstrap DL rarely changes conclusions (although higher heterogeneity estimates and found in around 20% more
meta-analysis
Conclusions change for:0.9% of analyses
No
Estimated heterogeneity ‘ignored’ by authors and a
fixed-effect model is chosenYes
Analysis with bootstrap DL rarely changes conclusions
Conclusions change for:2.4% of analyses
No
Analysis with bootstrap DL makes a difference in 1 in 5 analyses (as would analysis with standard DL
but to a smaller extent)
Conclusions change for:19.1% of analyses
Yes
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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0% 20% 40% 60% 80% 100%
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4th Qtr
[Replace, move, resize, or delete graphic, as necessary.]
[Replace, move, resize, or delete graphic, as necessary.]
[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database
FindingsCochrane database analysis
Detecting and accurately estimating heterogeneity in smallMAs is very difficult; yet for 53% of Cochrane MAs, k ≤ 3Estimates of zero heterogeneity should be a concernBootstrapped DL leads to a small improvement butproblem largely remains, especially for very small MAsAssume high levels of heterogeneity in sensitivity?Caution against ignoring heterogeneity when detectedDiscussed in:
Kontopantelis E, Springate DA and Reeves DA Re-Analysis of the Cochrane Library Data: The Dangersof Unobserved Heterogeneity in Meta-AnalysesPLOS ONE, 10.1371/journal.pone.0069930, 26 July 2013
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
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y Ca
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rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
0% 20% 40% 60% 80% 100%
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0% 20% 40% 60% 80% 100%
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Outline
1 Meta-analysis overview
2 Two-stage meta-analysis
3 One-stage meta-analysis
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
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rimar
y Ca
re R
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Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Forest plot
One advantage of two-stage meta-analysis is the ability toconvey information graphically through a forest plot
study effects available after the first stage of the process,and can be used to demonstrate the relative strength of theintervention in each study and across allinformative, easy to follow and particularly useful forreaders with little or no methodological experiencekey feature of meta-analysis and always presented whentwo-stage meta-analyses are performed
In one-stage meta-analysis, only the overall effect iscalculated and creating a forest-plot is not straightforward
Enter ipdforest
Kontopantelis Software and model selection challenges in meta-analysis
Heal
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cien
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Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Outline
1 Meta-analysis overview
2 Two-stage meta-analysis
3 One-stage meta-analysisA practical guideipdforestExamplesPower
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
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cien
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y Ca
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esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
The hypothetical study
Individual patient data from randomised controlled trialsFor each trial we have
a binary control/intervention membership variablebaseline and follow-up data for the continuous outcomecovariates
Assume measurements consistent across trials andstandardisation is not requiredWe will explore linear random-effects models with thextmixed command; application to the logistic case usingxtmelogit should be straightforwardIn the models that follow, in general, we denote fixedeffects with ‘γ’s and random effects with ‘β’s
Kontopantelis Software and model selection challenges in meta-analysis
Heal
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Grou
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[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
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RESULTS
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CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Model 1fixed common intercept; random treatment effect; fixed effect for baseline
Yi j = γ0 + β1jgroupi j + γ2Ybi j + εi j εi j ∼ N(0, σ2j )
β1j = γ1 + u1j u1j ∼ N(0, τ12)
i : the patientj : the trialYi j : the outcomeγ0: fixed common interceptβ1j : random treatmenteffect for trial jγ1: mean treatment effectgroupi j : group membership
γ2: fixed baseline effectYbi j : baseline scoreu1j : random treatmenteffect for trial jτ1
2: between trial varianceεi j : error termσ2
j : within trial variance fortrial j
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
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rimar
y Ca
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esea
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Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Model 1fixed common intercept; random treatment effect; fixed effect for baseline
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
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esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Model basicsCommon fixed-effect
05
101520253035
1 2 3 4
Study
Fixed-effectcommon intercept
Kontopantelis Software and model selection challenges in meta-analysis
Heal
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y Ca
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Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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[Add key point.]
[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Model basicsRandom-effect
05
101520253035
1 2 3 4
Study
Random-effectsintercept
Random-effectsintercept level
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
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[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
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[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Model basicsTrial specific fixed-effect
05
101520253035
1 2 3 4
Study
Fixed-effect trial-specific intercept
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Model 1fixed common intercept; random treatment effect; fixed effect for baseline
Possibly the simplest approachIn Stata it can be expressed as
xtmixed Y i.group Yb || studyid:group,nocons
05
101520253035
1 2 3 4
Study
Fixed-effectcommon intercept
05
101520253035
1 2 3 4
Study
Random-effectstreatment
Random-effectstreatmentmean
05
101520253035
1 2 3 4
Study
Fixed-effectcommon covariate
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.]
[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Model 2fixed trial intercepts; random treatment effect; fixed trial effects for baseline
Common intercept & fixed baseline difficult to justifyA more accepted model:
xtmixed Y i.group i.studyid Yb1 Yb2 Yb3 Yb4|| studyid:group, nocons
05
101520253035
1 2 3 4
Study
Fixed-effect trial-specific intercept
05
101520253035
1 2 3 4
Study
Random-effectstreatment
Random-effectstreatmentmean
05
101520253035
1 2 3 4
Study
Fixed-effect trial-specific covariate
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.]
[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Model 3random trial intercept; random treatment effect; fixed trial-specific effects for baseline
Another possibility, althought contentious, is to assume trialintercepts are random (e.g. multi-centre trial):
xtmixed Y i.group Yb1 Yb2 Yb3 Yb4 ||studyid:group, cov(uns)
05
101520253035
1 2 3 4
Study
Random-effectsintercept
Random-effectsintercept level
05
101520253035
1 2 3 4
Study
Random-effectstreatment
Random-effectstreatmentmean
05
101520253035
1 2 3 4
Study
Fixed-effectcommon covariate
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
0% 20% 40% 60% 80% 100%
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0% 20% 40% 60% 80% 100%
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[Replace, move, resize, or delete graphic, as necessary.]
[Replace, move, resize, or delete graphic, as necessary.]
[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Model 4random trial intercept; random treatment effect; random effects for baseline
The baseline could also have been modelled as arandom-effect:
xtmixed Y i.group Yb || studyid:group Yb,cov(uns)
05
101520253035
1 2 3 4
Study
Random-effectsintercept
Random-effectsintercept level
05
101520253035
1 2 3 4
Study
Random-effectstreatment
Random-effectstreatmentmean
05
101520253035
1 2 3 4
Study
Random-effectscovariate
Random-effectscovariate level
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.]
[Replace, move, resize, or delete graphic, as necessary.]
[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Interactions and covariates
One of the major advantages of IPD meta-analysesA covariate or an interaction term can be modelled as afixed or random effectIn Stata expressed as:
xtmixed Y i.group i.studyid Yb1 Yb2 Yb3 Yb4age i.group#c.age || studyid:group, nocons
If modelled as a random effect, non-convergence issuesmore likely to be encountered
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
0% 20% 40% 60% 80% 100%
1st Qtr
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0% 20% 40% 60% 80% 100%
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[Replace, move, resize, or delete graphic, as necessary.]
[Replace, move, resize, or delete graphic, as necessary.]
[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add title, if necessary.]
[Add key point.]
[Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Outline
1 Meta-analysis overview
2 Two-stage meta-analysis
3 One-stage meta-analysisA practical guideipdforestExamplesPower
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
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cien
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[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
General
ipdforest is issued following an IPD meta-analysis thatuses mixed effects two-level regression, with patientsnested within trials and a
linear model (xtmixed)logistic model (xtmelogit)
Provides a meta-analysis summary table and a forest plotTrial effects are calculated within ipdforest
Can calculate and report both main and interaction effectsOverall effect(s) & variance estimates extracted frompreceding regressionCalculates and reports heterogeneity
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
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CONCLUSIONS
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RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Outline
1 Meta-analysis overview
2 Two-stage meta-analysis
3 One-stage meta-analysisA practical guideipdforestExamplesPower
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
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cien
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rimar
y Ca
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esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Depression intervention
We apply the ipdforest command to a dataset of 4depression intervention trialsComplete information in terms of age, gender,control/intervention group membership, continuousoutcome baseline and endpoint values for 518 patientsFake author names and generated random continuous &binary outcome variables, while keeping the covariates attheir actual valuesIntroduced correlation between baseline and endpointscores and between-trial variabilityLogistic IPD meta-analysis, followed by ipdforest
Kontopantelis Software and model selection challenges in meta-analysis
Heal
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cien
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rimar
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Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Dataset
. use ipdforest_example.dta,
. describe
Contains data from ipdforest_example.dtaobs: 518vars: 17 6 Feb 2012 11:14size: 20,202
storage display valuevariable name type format label variable label
studyid byte %22.0g stid Study identifierpatid int %8.0g Patient identifiergroup byte %20.0g grplbl Intervention/control groupsex byte %10.0g sexlbl Genderage float %10.0g Age in yearsdepB byte %9.0g Binary outcome, endpointdepBbas byte %9.0g Binary outcome, baselinedepBbas1 byte %9.0g Bin outcome baseline, trial 1depBbas2 byte %9.0g Bin outcome baseline, trial 2depBbas5 byte %9.0g Bin outcome baseline, trial 5depBbas9 byte %9.0g Bin outcome baseline, trial 9depC float %9.0g Continuous outcome, endpointdepCbas float %9.0g Continuous outcome, baselinedepCbas1 float %9.0g Cont outcome baseline, trial 1depCbas2 float %9.0g Cont outcome baseline, trial 2depCbas5 float %9.0g Cont outcome baseline, trial 5depCbas9 float %9.0g Cont outcome baseline, trial 9
Sorted by: studyid patid
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
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rimar
y Ca
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esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
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RESULTS
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CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
ME logistic regression model - continuous interactionfixed trial intercepts; fixed trial effects for baseline; random treatment and age effects
. xtmelogit depB group agec sex i.studyid depBbas1 depBbas2 depBbas5 depBbas9 i> .group#c.agec || studyid:group agec, var nocons or
Mixed-effects logistic regression Number of obs = 518Group variable: studyid Number of groups = 4
Obs per group: min = 42avg = 129.5max = 214
Integration points = 7 Wald chi2(11) = 42.06Log likelihood = -326.55747 Prob > chi2 = 0.0000
depB Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
group 1.840804 .3666167 3.06 0.002 1.245894 2.71978agec .9867902 .0119059 -1.10 0.270 .9637288 1.010403sex .7117592 .1540753 -1.57 0.116 .4656639 1.087912
studyid2 1.050007 .5725516 0.09 0.929 .3606166 3.0573035 .8014551 .5894511 -0.30 0.763 .189601 3.3877999 1.281413 .6886057 0.46 0.644 .4469619 3.673735
depBbas1 3.152908 1.495281 2.42 0.015 1.244587 7.987253depBbas2 4.480302 1.863908 3.60 0.000 1.982385 10.12574depBbas5 2.387336 1.722993 1.21 0.228 .5802064 9.823007depBbas9 1.881203 .7086507 1.68 0.093 .8990569 3.936262
group#c.agec1 1.011776 .0163748 0.72 0.469 .9801858 1.044385
_cons .5533714 .2398342 -1.37 0.172 .2366472 1.293993
Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]
studyid: Independentvar(group) 8.86e-21 2.43e-11 0 .var(agec) 5.99e-18 4.40e-11 0 .
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
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esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
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RESULTS
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CONCLUSIONS
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[Graphic title]
RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
ipdforestmodelling main effect and interaction
. ipdforest group, fe(sex) re(agec) ia(agec) or
One-stage meta-analysis results using xtmelogit (ML method) and ipdforestMain effect (group)
Study Effect [95% Conf. Interval] % Weight
Hart 2005 2.118 0.942 4.765 19.88Richards 2004 2.722 1.336 5.545 30.69Silva 2008 2.690 0.748 9.676 8.11Kompany 2009 1.895 0.969 3.707 41.31
Overall effect 1.841 1.246 2.720 100.00
One-stage meta-analysis results using xtmelogit (ML method) and ipdforestInteraction effect (group x agec)
Study Effect [95% Conf. Interval] % Weight
Hart 2005 0.972 0.901 1.049 19.88Richards 2004 0.995 0.937 1.055 30.69Silva 2008 0.987 0.888 1.098 8.11Kompany 2009 1.077 1.015 1.144 41.31
Overall effect 1.012 0.980 1.044 100.00
Heterogeneity Measures
value [95% Conf. Interval]
I^2 (%) .H^2 .tau^2 est 0.000 0.000 .
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
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CONCLUSIONS
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RESULTS
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Forest plotsmain effect and interaction
Overall effect
Kompany 2009
Silva 2008
Richards 2004
Hart 2005
Stu
dies
0 2 3 4 5 6 7 8 9 101Effect sizes and CIs (ORs)
Main effect (group)
Overall effect
Kompany 2009
Silva 2008
Richards 2004
Hart 2005
Stu
dies
0 .2 .4 .6 .8 1.2 1.41Effect sizes and CIs (ORs)
Interaction effect (group x agec)
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Outline
1 Meta-analysis overview
2 Two-stage meta-analysis
3 One-stage meta-analysisA practical guideipdforestExamplesPower
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
Power calculationsto detect a moderator effect
The best approach is through simulationsAs always, numerous assumptions need to be made
effect sizes (main and interaction)exposure and covariate distributionscorrelation between variableswithin-study (error) variancebetween-study (error) variance - ICC
Generate 1000s of data sets using the assumed model(s)Estimate what % of these give a significant p-value for theinteractionCan be trial and error till desired power level achieved
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
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[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
A practical guideipdforestExamplesPower
ipdpower
Currently under reviewStata program that allows for:
continuous, binary or count outcomevarious random effects as discussed in ipdforestvarious distributional assumptions (e.g. skew-normaloutcome)main effects and interactions (moderators effects)
Reports power, coverage, effect estimates andheterogeneity estimates
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
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[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
Outline
1 Meta-analysis overview
2 Two-stage meta-analysis
3 One-stage meta-analysis
4 Summary
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
Two-stage meta-analysis
MetaEasy can help organise meta-analysis and can bevery useful if need to combine continuous and binaryoutcomes.Methods implemented in Stata under metaeff and metaanZero heterogeneity estimate is a reason to worry;heterogeneity might be there but we cannot measure oraccount for in the modelIf detected, even if very small, use a random-effects modelfor generalisability?All methods likely to miss or underestimate heterogeneity
use sensitivity analysis approach?
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
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RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
[Add title, if necessary.]
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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis
Summary
One-stage meta-analysis
A few different approaches exist for conducting one-stageIPD meta-analysisStata can cope through the xtmixed and the xtmelogitcommandsThe ipdforest command aims to help meta-analysts
calculate trial effectsdisplay results in standard meta-analysis tablesproduce familiar and ‘expected’ forest-plots
The easiest way to calculate power to detect complexeffects is through simulations
see ipdpower, under review
Kontopantelis Software and model selection challenges in meta-analysis
Heal
th S
cien
ces P
rimar
y Ca
re R
esea
rch
Grou
p
[Poster title]
ABSTRACT TITLE: [Add text here.]
BACKGROUND: [Add text here.]
OBJECTIVE: [Add text here.]
METHODS: [Add text here.]
RESULTS: [Add text here.]
CONCLUSIONS: [Add text here.]
BACKGROUND [Add title, if necessary.]
Label One
Label Two
Label Three
Label Four
[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]
OBJECTIVE
[Repeat objective from above.]
METHODS
[Add title, if necessary.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add key point.] [Add description of key point.]
[Add title, if necessary.]
[Add key point.] [Sub-bullet] [Sub-bullet]
[Add key point.]
[Sub-bullet] [Sub-bullet]
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[Add key point.]
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[Sub-bullet] [Sub-bullet]
RESULTS
[Add title, if necessary.]
[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]
CONCLUSIONS
[Add text as bulleted list or a paragraph.] [Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Add key point.]
[Graphic title]
RESULTS
[Graphic title] [Graphic title]
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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]
For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]
Excepteur Sint Lkl
(n=212) Controls
(n=27)
Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
Unknown
79 (373%) 121 (571%)
2 (09%) 0
1 (05%) 9 (42%)
7 (259%) 18 (667%)
0 1 (37%) 1 (37%)
0
Proident F
Lorem
106 (50%) 101 (476%)
5 (24%)
17 (63%) 10 (37%)
Nostrud: N Y
Unknown
172 (811%) 22 (104%) 18 (85%)
26 (963%) 0
1 (37%)
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AppendixThank you!References
Comments, suggestions:[email protected]
Kontopantelis Software and model selection challenges in meta-analysis
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Excepteur Sint Lkl
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Lorum Wt (kg) 18 (SD 10) 29 (SD 07)
Ipsum (wk) 31 (SD 5) 37 (SD 2)
Irure: B W H HB O
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79 (373%) 121 (571%)
2 (09%) 0
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7 (259%) 18 (667%)
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AppendixThank you!References
JSS Journal of Statistical SoftwareApril 2009, Volume 30, Issue 7. http://www.jstatsoft.org/
MetaEasy: A Meta-Analysis Add-In for Microsoft
Excel
Evangelos KontopantelisNational Primary Care Research
and Development Centre
David ReevesNational Primary Care Research
and Development Centre
Abstract
Meta-analysis is a statistical methodology that combines or integrates the results ofseveral independent clinical trials considered by the analyst to be ‘combinable’ (Huque1988). However, completeness and user-friendliness are uncommon both in specialisedmeta-analysis software packages and in mainstream statistical packages that have to relyon user-written commands. We implemented the meta-analysis methodology in an Mi-crosoft Excel add-in which is freely available and incorporates more meta-analysis models(including the iterative maximum likelihood and profile likelihood) than are usually avail-able, while paying particular attention to the user-friendliness of the package.
Keywords: meta-analysis, forest plot, Excel, VBA, maximum likelihood, profile likelihood.
1. Introduction
Meta-analysis can be defined as the statistical analysis of a large collection of analysis resultsfrom individual studies - usually Randomised Controlled Trials (RCTs) - for the purpose ofintegrating the findings (Glass 1976). Although the debate regarding the quality and ap-plication caveats of the method is ongoing (Egger and Smith 1997; Bailar 1997), a Medline(http://medline.cos.com/) search by the authors reveals that the number of meta-analysisstudies published in peer-reviewed journals seems to be growing exponentially (Figure 1).Published meta-analysis studies (search criterion: Publication Type=meta-analysis) haverisen from 274 in 1990 to 2138 in 2005, while published work that is either a meta-analysisor deals with meta-analysis issues (search criterion: Keyword=meta-analysis) has increasedfrom 329 to 3350, in the same period.
A major issue in meta-analysis is the almost inevitable clinical or methodological heterogene-ity among the combined studies (Eysenck 1994). If the study results differ greatly (large
A Re-Analysis of the Cochrane Library Data: The Dangersof Unobserved Heterogeneity in Meta-AnalysesEvangelos Kontopantelis1,2,3*, David A. Springate1,2, David Reeves1,2
1 Centre for Primary Care, NIHR School for Primary Care Research, Institute of Population Health, University of Manchester, Manchester, United Kingdom, 2 Centre for
Biostatistics, Institute of Population Health, University of Manchester, Manchester, United Kingdom, 3 Centre for Health Informatics, Institute of Population Health,
University of Manchester, Manchester, United Kingdom
Abstract
Background: Heterogeneity has a key role in meta-analysis methods and can greatly affect conclusions. However, true levelsof heterogeneity are unknown and often researchers assume homogeneity. We aim to: a) investigate the prevalence ofunobserved heterogeneity and the validity of the assumption of homogeneity; b) assess the performance of various meta-analysis methods; c) apply the findings to published meta-analyses.
Methods and Findings: We accessed 57,397 meta-analyses, available in the Cochrane Library in August 2012. Usingsimulated data we assessed the performance of various meta-analysis methods in different scenarios. The prevalence of azero heterogeneity estimate in the simulated scenarios was compared with that in the Cochrane data, to estimate thedegree of unobserved heterogeneity in the latter. We re-analysed all meta-analyses using all methods and assessed thesensitivity of the statistical conclusions. Levels of unobserved heterogeneity in the Cochrane data appeared to be high,especially for small meta-analyses. A bootstrapped version of the DerSimonian-Laird approach performed best in bothdetecting heterogeneity and in returning more accurate overall effect estimates. Re-analysing all meta-analyses with thisnew method we found that in cases where heterogeneity had originally been detected but ignored, 17–20% of thestatistical conclusions changed. Rates were much lower where the original analysis did not detect heterogeneity or took itinto account, between 1% and 3%.
Conclusions: When evidence for heterogeneity is lacking, standard practice is to assume homogeneity and apply a simplerfixed-effect meta-analysis. We find that assuming homogeneity often results in a misleading analysis, since heterogeneity isvery likely present but undetected. Our new method represents a small improvement but the problem largely remains,especially for very small meta-analyses. One solution is to test the sensitivity of the meta-analysis conclusions to assumedmoderate and large degrees of heterogeneity. Equally, whenever heterogeneity is detected, it should not be ignored.
Citation: Kontopantelis E, Springate DA, Reeves D (2013) A Re-Analysis of the Cochrane Library Data: The Dangers of Unobserved Heterogeneity in Meta-Analyses. PLoS ONE 8(7): e69930. doi:10.1371/journal.pone.0069930
Editor: Tim Friede, University Medical Center Gottingen, Germany
Received February 20, 2013; Accepted June 13, 2013; Published July 26, 2013
Copyright: � 2013 Kontopantelis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: EK was partly supported by a National Institute for Health Research (NIHR) School for Primary Care Research fellowship in primary health care. Thefunders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding received forthis study.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: [email protected]
Introduction
Meta-analysis (MA), the methodologies of synthesising existing
evidence to answer a clinical or other research question, is a
relatively young and dynamic area of research. The furore of
methodological activity reflects the clinical importance of meta-
analysis and its potential to provide conclusive answers, rather
than incremental knowledge contributions, much more cheaply
than a new large Randomised Clinical Trial (RCT).
The best analysis approach is an Individual Patient Data (IPD)
meta-analysis, which requires access to patient level data and
considerably more effort (to obtain the datasets mainly). However,
with IPD data, clinical and methodological heterogeneity,
arguably the biggest concern for meta-analysts, can be addressed
through patient-level covariate controlling or subgroup analyses
when covariate data are not available across all studies.
When the original data are unavailable, researchers have to
combine the evidence in a two stage process, retrieving the
relevant summary effects statistics from publications and using a
suitable meta-analysis model to calculate an overall effect estimate
mm. Model selection depends on the estimated heterogeneity, or
between-study variance, and its presence usually leads to the
adoption of a random-effects (RE) model. The alternative, the
fixed-effects model (FE), is used when meta-analysts, for theoretical
or practical reasons, decide not to adjust for heterogeneity, or have
assumed or estimated the between-study variability to be zero.
Different approaches exist for combining individual study results
into an overall estimate of effect under the fixed- or random-effects
assumptions: inverse variance, Mantel-Haenszel and Peto [1].
Inverse variance approaches are the most flexible and are
suitable for continuous or dichotomous data through a fixed-effect
or one of numerous random-effects methods. The DerSimonian
and Laird [2] method (DL), a moment-based estimator, is the
PLOS ONE | www.plosone.org 1 July 2013 | Volume 8 | Issue 7 | e69930
The Stata Journal (2010)10, Number 3, pp. 395–407
metaan: Random-effects meta-analysis
Evangelos KontopantelisNational Primary Care
Research & Development CentreUniversity of Manchester
Manchester, UK
David ReevesHealth Sciences Primary Care
Research GroupUniversity of Manchester
Manchester, UK
Abstract. This article describes the new meta-analysis command metaan, whichcan be used to perform fixed- or random-effects meta-analysis. Besides the stan-dard DerSimonian and Laird approach, metaan offers a wide choice of availablemodels: maximum likelihood, profile likelihood, restricted maximum likelihood,and a permutation model. The command reports a variety of heterogeneity mea-sures, including Cochran’s Q, I2, H2
M , and the between-studies variance estimatebτ2. A forest plot and a graph of the maximum likelihood function can also begenerated.
Keywords: st0201, metaan, meta-analysis, random effect, effect size, maximumlikelihood, profile likelihood, restricted maximum likelihood, REML, permutationmodel, forest plot
1 Introduction
Meta-analysis is a statistical methodology that integrates the results of several inde-pendent clinical trials in general that are considered by the analyst to be “combinable”(Huque 1988). Usually, this is a two-stage process: in the first stage, the appropriatesummary statistic for each study is estimated; then in the second stage, these statis-tics are combined into a weighted average. Individual patient data (IPD) methodsexist for combining and meta-analyzing data across studies at the individual patientlevel. An IPD analysis provides advantages such as standardization (of marker values,outcome definitions, etc.), follow-up information updating, detailed data-checking, sub-group analyses, and the ability to include participant-level covariates (Stewart 1995;Lambert et al. 2002). However, individual observations are rarely available; addition-ally, if the main interest is in mean effects, then the two-stage and the IPD approachescan provide equivalent results (Olkin and Sampson 1998).
This article concerns itself with the second stage of the two-stage approach to meta-analysis. At this stage, researchers can select between two main approaches—the fixed-effects (FE) or the random-effects model—in their efforts to combine the study-levelsummary estimates and calculate an overall average effect. The FE model is simplerand assumes the true effect to be the same (homogeneous) across studies. However, ho-mogeneity has been found to be the exception rather than the rule, and some degree oftrue effect variability between studies is to be expected (Thompson and Pocock 1991).Two sorts of between-studies heterogeneity exist: clinical heterogeneity stems from dif-
c© 2010 StataCorp LP st0201
JSS Journal of Statistical SoftwareMMMMMM YYYY, Volume VV, Issue II. http://www.jstatsoft.org/
Simulation-based power calculations for mixed
effects modelling: ipdpower in Stata
Evangelos KontopantelisNIHR School for Primary Care Research
University of Manchester
David A SpringateInstitute of Population Health
University of Manchester
Rosa ParisiManchester Pharmacy School
University of Manchester
David ReevesInstitute of Population Health
University of Manchester
Abstract
Simulations are a practical and reliable approach to power calculations, especially formulti-level mixed effects models where the analytic solutions can be very complex. Inaddition, power calculations are model-specific and multi-level mixed effects models aredefined by a plethora of parameters. In other words, model variations in this context arenumerous and so are the tailored algebraic calculations. This article describes ipdpower inStata, a new simulations-based command that calculates power for mixed effects two-leveldata structures. Although the command was developed having individual patient datameta-analyses and primary care databases analyses in mind, where patients are nestedwithin studies and general practices respectively, the methods apply to any two-levelstructure.
Keywords: Stata, ipdpower, power, coverage, meta analysis, multi level, mixed effects, randomeffects, individual patient data, IPD, primary care databases, PCD.
1. Introduction
The size of primary care databases (PCDs) allows for investigations that cannot normallybe undertaken in much smaller randomised controlled trials (RCTs), such as the moderatingeffect of a patient characteristic on the effect of an intervention. However, researchers quiteoften underestimate the numbers needed to detect such effects and assume that the size ofthe database alone guarantees adequate power for any type of investigation. The essential
Article
Performance of statisticalmethods for meta-analysiswhen true study effects arenon-normally distributed: Asimulation study
Evangelos Kontopantelis1 and David Reeves2
Abstract
Meta-analysis (MA) is a statistical methodology that combines the results of several independent studies
considered by the analyst to be ‘combinable’. The simplest approach, the fixed-effects (FE) model, assumes
the true effect to be the same in all studies, while the random-effects (RE) family of models allows the true
effect to vary across studies. However, all methods are only correct asymptotically, while some RE models
assume that the true effects are normally distributed. In practice, MA methods are frequently applied
when study numbers are small and the normality of the effect distribution unknown or unlikely. In this
article, we discuss the performance of the FE approach and seven frequentist RE MA methods:
DerSimonian–Laird, Q-based, maximum likelihood, profile likelihood, Biggerstaff–Tweedie, Sidik–
Jonkman and Follmann–Proschan. We covered numerous scenarios by varying the MA sizes (small to
moderate), the degree of heterogeneity (zero to very large) and the distribution of the effect sizes
(normal, skew-normal and ‘extremely’ non-normal). Performance was evaluated in terms of coverage
(Type I error), power (Type II error) and overall effect estimation (accuracy of point estimates and error
intervals).
Keywords
meta-analysis, non-normal, profile likelihood, power, coverage, simulation, DerSimonian–Laird,
Biggestaff–Tweedie, Sidik–Jonkman, permutations
1National Primary Care Research and Development Centre, University of Manchester, Williamson Building, 5th Floor, Oxford Road,
M13 9PL, UK2Health Sciences Primary Care Research Group, University of Manchester, Williamson Building, 5th Floor, Oxford Road, M13 9PL,
UK
Corresponding author:
Evangelos Kontopantelis, National Primary Care Research and Development Centre, University of Manchester, Williamson Building,
5th Floor, Oxford Road, M13 9PL, UK
Email: [email protected]
Statistical Methods in Medical Research
21(4) 409–426
! The Author(s) 2010
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DOI: 10.1177/0962280210392008
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Kontopantelis Software and model selection challenges in meta-analysis