supplementary appendix - the lancet€¦ · supplementary appendix ... ketonal or fastum gel or...

Supplementary appendixThis appendix formed part of the original submission and has been peer reviewed. We post it as supplied by the authors.

Supplement to: da Costa BR, Reichenbach S, Keller N, Nartey L, Wandel S, Jüni P, Trelle S. Effectiveness of non-steroidal anti-inflammatory drugs for the treatment of pain in knee and hip osteoarthritis: a network meta-analysis. Lancet 2016; published online March 17. http://dx.doi.org/10.1016/S0140-6736(16)30002-2.

WEB-APPENDIX

Effectiveness of Non-Steroidal Anti-Inflammatory Drugs for the Treatment of

Osteoarthritis Pain: A Network Meta-Analysis

Table of Contents Web-appendix 1. Search Strategy ........................................................................................................... 2

Web-appendix 2. Additional methods ................................................................................................... 10

Main model: Random walk ................................................................................................................. 10

Alternative model 1: Full exchangeability ......................................................................................... 13

Alternative model 2: Unconstrained baseline, random-walk on treatment effect ....................... 15

References ............................................................................................................................................ 18

Web-appendix 3. Definitions used to classify trials according to components of methodological

quality ......................................................................................................................................................... 19

Web-appendix 4. Flow diagram and references of included trials .................................................... 20

Web-appendix 5. Treatment effect according to time point ............................................................... 26

Web-appendix 6. Assessment of Model Fit .......................................................................................... 29

Web-appendix 7. Inconsistency ............................................................................................................. 30

Web-appendix 8. Analyses using alternative models for network meta-analysis .......................... 34

Web-appendix 9. Results of adjusted models ..................................................................................... 36

Web-appendix 10. Comparison adjusted funnel plots ........................................................................ 41

WEB APPENDIX

Effectiveness of Non-Steroidal Anti-Inflammatory Drugs for the Treatment of Osteoarthritis Pain:

A Network Meta-Analysis

2 | 42

Web-appendix 1. Search Strategy MEDLINE and EMBASE were searched through the OVID platform (www.ovid.com). The Cochrane

Central Register of Controlled Trials was searched through the Cochrane library

(http://www.cochranelibrary.com/). The search was last updated on February 24 2015.

Cochrane Central Register of Controlled Trials (CENTRAL)

Step Search Strategy

1 (osteoarthriti* OR osteoarthro* OR gonarthriti* OR gonarthro* OR coxarthriti* OR coxarthro*

OR arthros* OR arthrot*) in Clinical Trials

2 MeSH descriptor Osteoarthritis explode all trees

3 1 OR 2

Ovid (MEDLINE and EMBASE)

Step Search Strategy

1 randomized controlled trial.pt.

2 controlled clinical trial.pt.

3 randomized controlled trial.sh.

4 random allocation.sh.

5 double blind method.sh.

6 single blind method.sh.

7 clinical trial.pt.

8 exp clinical trial/

9 (clin$ adj25 trial$).ti,ab.

10 ((singl$ or doubl$ or trebl$ or tripl$) adj25 (blind$ or mask$)).ti,ab.

11 placebos.sh.

12 placebo$.ti,ab.

13 random$.ti,ab.

14 research design.sh.

15 comparative study.sh.

16 exp evaluation studies/

17 follow up studies.sh.

18 prospective studies.sh.

19 (control$ or prospectiv$ or volunteer$).ti,ab.

20 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19

http://www.ovid.com/

http://www.cochranelibrary.com/

WEB APPENDIX



3 | 42

21

(osteoarthriti$ or osteoarthro$ or gonarthriti$ or gonarthro$ or coxarthriti$ or coxarthro$ or arthros$ or arthrot$).mp. or exp Osteoarthritis/ [mp=title, abstract, original title, name of

substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]

22

(((Act-3 or Actron or Adex or Arinac or Advifen or Advil or Aktren or Alaxan or Alges-X or Algifor or Algoflex or Algofren or Alindrin or Alivium or Anadin Ibuprofen or Anadin or Arthrofen or Artofen or Betagesic or Betaprofen or Bonifen or Brufen or Bugesic or Buplex or Burana or Buprovil or Caldolor or Calprofen or Combiflam or Dalsy or Dismenol or Diverin or Dolgit or

Dolofort or Doloraz or Dolormin or Dolo-Spedifen or Dorival or Easofen or Ebufac or EmuProfen or Espidifen or Eve or Fenbid or Fenpaed or Finalflex or Galprofen or Hedex or Herron Blue or i-

profen or Ibalgin or Ibrofen) and Ibugan) or IBU or IBUFEN or ibufen or Ibugel or Ibuflam or Ibugesic or IbuHEXAL or Ibuleve or Ibum or Ibumax or Ibumetin or Ibumidol or Ibupain or

Ibuprofen or Ibuprofene or Ibuprom or Ibuprox or IBU-Ratiopharm or Ibuprosyn or Ibustar or Ibu-Vivimed or Ibux or Ibuxin or Ipren or Kratalgin or Lotem or Medicol or Moment or Motrin or Mypaid or Myprodol or Narfen or Naron Ace or Neobrufen or Neofen or Norvectan or Nuprin or Nureflex or Nurofen or Orbifen or Panafen or Perifar or Profin or Ranfen or Rapidol or Ratiodolor

or Rimafen or Salvarina or Solpaflex or Spedifen or Speedpain NANO or Spidifen or Tefin or Unafen or Upfen).mp. [mp=title, abstract, original title, name of substance word, subject

heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]

23

(Anyfen or Artoflex or Atriscal or Brutek-P or Cefalex or Daxfen or Decorafen or Deltaran or Dex-I or Dexibu or Dexifen or Dexifin or Dexigesic-P or Dexiron or DexOptifen or Dexpro or Dirol or

Dolomagon or Eu-Med or Exifen or Fendex or Fenextra or Flamex-Dx or Inflam-D or Maxibupen or Pentaud or Purifen or Seractil or Seractil or Seractiv or Sibet or Sibet P or Speedfen or Tradil

or Xflam or Xibu or Dexibuprofen).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word,

rare disease supplementary concept word, unique identifier]

24

(Aleve or Anaprox or Apronax or Naprelan or Naprosyn or Aflaxen or Flanax or Naproxen).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept

word, unique identifier]

25 (Nalfon or Fenoprofen or Fenopron).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word,


26

(Ketoprofen or Fastum or Spondylon or Orudis or Oruvail or Ketoflam or Oruvail or Keto or Ketonal or Fastum Gel or Ketorin or Keto or Ketomex or Orudis or Profenid or Bi-Profenid or

Ketum or Ketodol or Lasonil or Orudis or Oki or Knavon or Ketonal or Arthril or Zon or Orudis or OKI or Actron or Ketoprofeno).mp. [mp=title, abstract, original title, name of substance word,

subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]

27

(Actidex or Dexomen or Dolmen or Fen Li or Keral or Ketesse or Keto-D or Ketron-D or Kitex or Kynol or Menadex or Tador or Adolquir or Arveles or Desketo or Dexadol or Dexak or Dexalgin or

Dexofen or Dexoket or Dolmen or Enangel or Enantyum or Keral or Ketesgel or Ketesse or Ketodex or Kui La Lan or Algifort or Nosatel or Quiralam or Stadium or Sympal or Viaxal or

Youbaifen or Dexketoprofen).mp. [mp=title, abstract, original title, name of substance word,

WEB APPENDIX



4 | 42


28 (Ansaid or Flurbiprofen).mp. [mp=title, abstract, original title, name of substance word, subject


29 (Daypro or Oxaprozin).mp. [mp=title, abstract, original title, name of substance word, subject


30

(An Pu Luo or Analoxo or Bei Luo or Cobalokinin or Confatanin or Kentan or Letrac or Lexofen or Lobafen or Lobu or Lockpain or Locpren or Loxefin or Loxfen or Loxfenine or Loxipain or

Loxipronal or Loxodfen or Loxofen or Loxomarin or Loxonin Dongwha or Loxonin or Loxot or Loxpain or Luo Lie Tong or Luo Na or Nobfen or Okiminas or Oxeno or Ponapelt or Qing Fu or

Ringereaze or Rokifen or Roseol or Roxonin or RoxsogenRuo Mai or Sai Ke Tong or San Yuan Shu or Xing or Sanloxo or Tsurumerin or Unastin or Weikang Dike or Zhen Tong Yan or Loxoprofen or Loxomac).mp. [mp=title, abstract, original title, name of substance word, subject heading word,

keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]

31 (Indocid or Indocin or Tivorbex or Indometacin).mp. [mp=title, abstract, original title, name of

substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]

32 (Tolectin or Tolmetin).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease

supplementary concept word, unique identifier]

33 (Clinoril or Sulindac).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease


34

(Etodolac or Lodine or Etova or Dualgan or Etodin or Etofree or Etopan or Flancox or Haipen or Proxym or Etol or Eccoxolac).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare

disease supplementary concept word, unique identifier]

35 (Toradol or Acular or Sprix or Ketorolac).mp. [mp=title, abstract, original title, name of substance

word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]

36

(Diclofenac or Aclonac or Cataflam or Voltaren or Cambia or Zipsor or Zorvolex or Lodex or Flector or Pennsaid or Solaraze).mp. [mp=title, abstract, original title, name of substance word,


37 (Aceclofenac or Hifenac or Cincofen or Nacsiv or Acenac).mp. [mp=title, abstract, original title,

name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]

38 (Nabumetone or Relafen or Relifex or Gambaran).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept

word, rare disease supplementary concept word, unique identifier]

39 ((Piroxicam or Candyl or Arantil or Brexidol or Brexin or Erazon or Exipan or Faxiden or Felden or Feldene or Feldoral or Flamexin or Hawksone or Hotemin or Lubor or Mobilis or Novo-Pirocam

WEB APPENDIX



5 | 42

or Pirox von ct or Proponol or Reumador or Remox or Roxam or Sinartrol or Toricam or Tracam or Ugesic or Veral) and Vurdon).mp. [mp=title, abstract, original title, name of substance word,


40 (Meloxicam or Mobic).mp. [mp=title, abstract, original title, name of substance word, subject


41

(Tenoxicam or Admiral or Alganex or Algin-Vek or Amcinafal or Apo-Tenoxicam or Artoxan or Artricom or Artroxicam or Aspagin or Atroxicam or Bart or Biodruff or Dolmen or Hobaticam or Indo-Bros or Inoten or Istotosal or Liaderyl or Memzotil or Mitrotil or Mobiflex or Neo Kalmol or Neo-Antiperstam or Neo-Endusix or Notritis or Octiveran or Oksamen or Oksamen-L or Oxaflam or Oxicam or Oxytel or Palitenox or Pilopil or Ponsolit or Portonal or Pycitil or Redac or Reutenox or Rheuflex or Rucornart or Seftil or Sinoral or Soral or Sutondin or Teconam or Teflan or Tenax

or Tencam or Tenocam or Tenoksan or Tenorix or Tenotec or Tenox or Tenoxicam Generis or Tenoxicam LPH or Tenoxicam Mintlab or Tenoxicam Mylan or Tenoxicam Sanitas or Tenoxicam

or Tenoxil or Tenoxin or Tentepanil or Tenxil or Texicam or Thenil or Tilarco or Tilatil or Tilcitin or Tilcotil or Tilflam or Tilko or Tilnoxcam or Tiloxican or Tobitil or Tonox or Toscacalm or Velasor or

Vienoks or Voir or Xicotil or Zibelant or Zikaral).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept


42 Droxicam.mp. [mp=title, abstract, original title, name of substance word, subject heading word,


43

(Lornoxicam or Acabel or Bosporon or Camri or Coslor-PDa Lu or Hypodol or Lezdes-P or Lolira-P or Lorcam or Lorchek-MR or Lorchek-P or Lornica or Lornofan or Lornoxi or Lornoxi-P or Lorsaid

or Lorter-P or Lorter-P Forte or Lorthox-P or Lorup Plus or Lorwalk or Luloxy-P or Mitilor or Movilor or Neucam or Neucam-P or Noxon or Powernac-L or Qinda or Quando or Renox-A or

Taigalor or Telos or Univer or Xafon or Xefo or Xefocam or Xilor or Zelorn or Zheng Ting or Chlortenoxicam).mp. [mp=title, abstract, original title, name of substance word, subject heading

word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]

44 (Isoxicam or Floxicam or Maxicam or Pacyl or Vectren).mp. [mp=title, abstract, original title,


45

(Mefenamic acid or Dolfenal or Beafemic or Mefalth or Mefalth or Mefivan or Ponstel or Ponstan or Ponstal or Parkemed or Revalan or Mafepain or Mefamed or Mephadolor or Meftal

or Dyfenamic or Potalon or Meyerdonal or Alfoxan or Fenagesic or Fenamin or Spiralgin or Almus or Mefacit or Pangesic Forte or Kalmadol or Fespa or Mefcox or Mefanorm or Meftal or

Mefkind or Vidan).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease


46

(Flufenamic acid or Antirheumatic or Flufemin or Kanyen or Mobilat or Suyanlen or Flufen or Opaferumin or Opyrin or Youfenil or Combec or Fenazol).mp. [mp=title, abstract, original title,


WEB APPENDIX



6 | 42

47

(Tolfenamic acid or Clotam or Clotan or Dolfenax or Fenamic or Flocur or Gantil or Migea or Purfalox or Te Fen Ta or Tolfedine or Tolfejec or Tolfine or Tolmic or Tufnil or Turbaund).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept

word, unique identifier]

48 (Celecoxib or Celebrex or Celebra).mp. [mp=title, abstract, original title, name of substance


49 (Rofecoxib or Vioxx or Ceoxx or Ceeoxx).mp. [mp=title, abstract, original title, name of substance


50 (Valdecoxib or Bextra).mp. [mp=title, abstract, original title, name of substance word, subject


51

(Parecoxib or Dynastat or Praxis or Rayzon or Valdy-P or Valto-P or Valus-P or Vorth-P or Dynastat or Pro-Bextra or Valdure).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word,


52 (Lumiracoxib or Prexige).mp. [mp=title, abstract, original title, name of substance word, subject


53

(Etoricoxib or Algirex or Algix or Arcoxia or ARCOXIA or Artorix or Auxib or Coxet or Coxia or Ebov or Eflam or Eoxy-H or Erofica or Eticox or Eto or Etocox or Etodolo or Etody or Etofan or

Etoflam or Etom or Etorica or Etorix or Etoshine or Etoxib or Etro or Etrobax or Exinef or Exxiv or Hicox or Hireto or Ifydrox or Kingcox or Kretos or L-Kon or M-Kon or Nucoxia or Nucoxia-P or

Nucoxia-SP or Oricox or Ranacox or Retoz or Ribox or Rito or Setorib or Tauxib or Tory or Tro or Tro-P or Turox or Xibra or Ezact or Coxyveen or Vargus or Starcox or Exxiv).mp. [mp=title,

abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique

identifier]

54 (Firocoxib or Equioxx or Previcox).mp. [mp=title, abstract, original title, name of substance word,


55

(Nimesulide or Actalide or Actasulid or Actinim or Aglonim or Ainex or Alencast or Algifeno or Algimesil or Algolider or Algosulid or Algover or Allgone or Ameolin or Amocetin or Amsolide or

Anjielishu or Antalor or Aponil or Areuma or Arslide or Artilide or Aulin or Auromedil or Auromelid or Bangni or Biolin or Bioxidol or Blunid or Ceralide-MD or Chemisulide or Cimelide or

Cliovyl or Coxtral or Deflogen or Degorflan or Dimesul or Discorid or Doloc or Dolonime or Dolostop or Domes or Drexel or Edrigyl or Efridol or Elinap or Emdon or Enetra or Erlecit or

Erreflog or Eskaflam or Fansulide or Feverein or Fladalgin or Flamide or Flogostop or Fu Mei Shu or Genlide or Hua Shuang Jie or Huang Xing Yun Shu or Igrexa RD or Igrexa or Isodol or Kartal or Kind-MD or Kitedo-Li or Lalide or Lanimex or Ledoren or Lemesil or Li Nuo Ke or Li Shu Tong or Lizepat or Lovirem or Lupisulide or Magenol or Maoxing or Melicat or Melimont or Mesilex or

Mesulid or Mesupon or Min-a-pon or Minesulin or Mosuolit or Motival or Moxenil or Multiformil or Myxina or Nam or Naofid or Nemil-Os or Neosili or Neosulida or Neptide or

WEB APPENDIX



7 | 42

Nerelid or New Nicip or Nexen or Niberan or Nibid or Nidol or Nidolon or Nile or Nilide or Nilup or Nimalgex or Nimdal or Nimdus or Nimed or Nimedol or Nimegel or Nimegesic or Nimel or Nimelid or Nimelide or Nimepast or Nimepis or Nimes or Nimesil or Nimesilam or Nimesul or

Nimesulid or Nimesulide or Nimesuvar or Nimesyl or Nimeter or Nimex or Nimfast or Nim-H or Nimica or Nimill or Nimind or Nimiz or Nimlide or Nimm or Nimo or Nimotas or Nimotas-CD or

Nimras-MD or Nims or Nimulex-FD or Nimulid or Nimustar or Nimutab or Nimvar-MD or Nimvista or Nincolide or Nisalgen or Nise or Nisulid or Nisural or Niyan or Nizer or Normosilen or

Novolid or NS100 Nimesulide or Nuleb or Numusyp or Nuo Zheng or Oronime or Orthobid or Orthobid Plus or Orthobid-MR or Pacimol Plus or Pantames or Pentra or Pu Fei Te or Pu Wei or

Pymenimis or Pyranim or Qnim or Quelodin or Reduben or Relisulide or Relmex or Remesulid or Remov or Reumolide or Ristolzit or Ritamine or Rolaket or Romasulid or Rui Li or Rui Pu Le or Rui

Zhi Li or Rui Zhi Qing or Sai Pu Da or Scaflam or Scaflan or Scalid or Seren-N or Serralex-NM or Severin or Severin NF or Shan Yi Ting or Si Rui or Solving or Soonil or Specilid or Suaron or Sulicox or Sulidamor or Sulidene or Sulidex or Sulidin or Sulidor or Sumo or Sumo or Sumo-MR or Sumo-Spas or Surong or Tengke or Tizanim or Tizu or Toro-Sanovel or Tranzicalm or Tredical or Ventor or Virobron or Volonten or Windose or Xianleke or Xilox or Ximede or Yi Mei Li or Yi Ya Xuan or

Zalide or Zimnil or Zitroflam or Zolan or Zolandin-MR or Maxsulid or Nimecox or Nimedex or Penalgin or Sulide or Coxtal or Sintalgin or Octaprin or Nimside or Nilsid or Donulide or Heugan

or Minapon or Nilden or Nimed or Nimedex or Nimesil or Nimulid or Nimutab or Nimdase or Nimopen or Nisulid or Nodard or Nicip or Nimcap or Nic or Nic-Spas or Novolid or Relmex or

Remisid or Scaflam or Scaflan or Sulidin or Xilox or Modact or Sulidene or Zolan or Lusemin or Medicox or Nidol or Nimalox or Nimesil or Nimotas or Nimulid or Nizer or Sorini or Ventor or

Vionim or Neolide or Willgo or Nims).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word,


56

(Clonixin or Adoplan or Algimate or Blonax or Celex or Clonalgin or Clonix or Clonixil or Clonixinato de lisina Larjan or Clonixinato de lisina Mintlab or Clonixinato de lisina Sanderson or

Clonixinato de lisina or Colmax or Dentagesic or Diclen or Disinal or Dolalgial or Dolamin or Dolex or Donodol or Dorix or Dorixina or Dorixina Forte or Dorixina or Dorixyl or Firac or Fultat or

Laboxina or Lafigesic or Ledosin or Libralgen or Lisina Clonixinato or Lixitin or Nefersil or Sedepron or Valoron).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease


57 (Licofelone or Harpagide).mp. [mp=title, abstract, original title, name of substance word, subject


58

(Pranoprofen or Cesflan or Difen Oculum or Difen or Flanin or Mulchina or Niflan or Oftalar or Pranofen or Pranoflog or Pranoprofen-Hai Shan Pharm or Pranopulin or Pranox or Pransus or

Proranon or Rupock or Valraizer).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare


59

(Tiaprofenic acid or Acide Tiaprofenique or Apo-Tiaprofenic or Atrogem or Bi-Tiapram or Flanid or Pain Will Pass or Refen or Sufen or Sulgem or Sulimen or Surgam Leciva or Surgam Retard or

Surgam or Surgamyl or Suroten or Surugafen or Sutain or Synotec or Tecon or Tia Ton or Tiagam or Tiap or Tiaprofenic Acid Zensei Yakuhin or Tifen or Tifenic or Tiogam or Tiprofen or Turganil or

WEB APPENDIX



8 | 42

Tiaprofen).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary

concept word, unique identifier]

60 (Alminoprofen or Minalfene).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare


61

(Zaltoprofen or Borbit or Peleton or Peon or Salafapinon or Soleng or Soleton or Soluirubin or Zaltoprofen Tatsumi Kagaku or Zatferon or Zyrogen).mp. [mp=title, abstract, original title, name

of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]

62 (Amfenac or Fenazox).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease


63

(Acemetacin or Acemet or Acemetacin-CT or Aceo or Baydol or Cobametacin or Emflex or Pranex or Pranex LP or Rantudal or Rantudil or Rheutrop or Shun Song or Tilur or Acemetadoc or

Acephlogont or Azeat or Gamespir or Oldan or Reudol or Ost-map).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol

supplementary concept word, rare disease supplementary concept word, unique identifier]

64

(Proglumetacin or Afloxan or Bruxel or Afloxan or Miridacin or Protaxil or Protaxon forte or Proxil or Tolindol).mp. [mp=title, abstract, original title, name of substance word, subject


65 (Mofezolac or Disopain).mp. [mp=title, abstract, original title, name of substance word, subject


66 (Ampiroxicam or Ampiroam or Flucam).mp. [mp=title, abstract, original title, name of substance


67 (Tiaramide or Bemidon or Solantal or Tiamide or Tiaram).mp. [mp=title, abstract, original title,


68 (Epirizole or Karmarte or Mebron or Melon or Meyuan or Miton).mp. [mp=title, abstract,

original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]

69 (Emorfazone or Emazon or Pentoil or Therapyace).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept


70 22 or 23 or 24 or 25 or 26 or 27 or 28 or 29 or 30 or 31 or 32 or 33 or 34 or 35 or 36 or 37 or 38 or 39 or 40 or 41 or 42 or 43 or 44 or 45 or 46 or 47 or 48 or 49 or 50 or 51 or 52 or 53 or 54 or

55 or 56 or 57 or 58 or 59 or 60 or 61 or 62 or 63 or 64 or 65 or 66 or 67 or 68 or 69

71 exp Anti-Inflammatory Agents, Non-Steroidal/ or ($steroidal and ($inflammatory or

$rheumatic)).mp. or ((Prostaglandin or Cyclooxygenase or Cyclo-Oxygenase or COX$) adj3 (Inhibitor$ or Antagonist$)).mp.

72 (Acetaminophen or Paracetamol or Tylenol or Datril or Panadol or Dafalgan or Panadol or Tipol or Calpol).mp. [mp=title, abstract, original title, name of substance word, subject heading word,

WEB APPENDIX



9 | 42


73 70 or 71 or 72

74 20 and 21 and 73

75 limit 74 to (humans and "all adult (19 plus years)")

76 limit 75 to yr="2009 -Current"

WEB APPENDIX



10 | 42

Web-appendix 2. Additional methods

Main model: Random walk

The assumption that the standardized effects at each timepoint are fully exchangeable around a

common standardized effect (alternative model 1) may be considered too restrictive, especially

when the course of the disease has a potential temporal pattern. We may weaken this

assumption by using a (first order) random walk. A random walk of first order assumes that any

adjacent standardized effects are more similar to each other than non-adjacent standardized

effects. Therefore, if there is an underlying temporal pattern, a random walk model may capture

this more appropriately than a full exchangeability model.

We use the following notation:

𝑖 denotes the study

𝑘 denotes the study arm

𝑡 denotes the timepoint

𝑡𝑟𝑡(𝑖, 𝑘) denotes the treatment in arm 𝑘 in study 𝑖

𝑏𝑠𝑙(𝑖) denotes the “baseline” treatment in study 𝑖

𝑔 = 1,2, …, are the treatments

𝑥𝑖 is a covariate on the study-level (e.g. adequate blinding)

In mathematical terms, the model is then written as follows:

𝑠𝑒𝑓𝑓𝑖𝑡𝑘| 𝜃𝑖𝑡𝑘, 𝑠𝑒𝑖𝑡𝑘~ 𝒩(𝜃𝑖𝑡𝑘 , 𝑠𝑒𝑖𝑡𝑘2 )

𝑓𝑜𝑟 𝑡 > 1

𝜃𝑖𝑡𝑘| 𝜃𝑖(𝑡−1)𝑘, 𝜏𝑡𝑖𝑚𝑒~ 𝒩(𝜃𝑖(𝑡−1)𝑘, 𝜏𝑡𝑖𝑚𝑒2 )

and

𝜃𝑖1𝑘 = {𝜇𝑖 𝑖𝑓 𝑡𝑟𝑡(𝑖, 𝑘) = 𝑏𝑠𝑙(𝑖)

𝜇𝑖 + δ𝑖𝑘 𝑒𝑙𝑠𝑒

𝛿𝑖𝑘| 𝑑𝑡𝑟𝑡(𝑖,𝑘), 𝑑𝑏𝑠𝑙(𝑖), 𝜏𝑅𝐸~ 𝒩(𝑑𝑡𝑟𝑡(𝑖,𝑘) − 𝑑𝑏𝑠𝑙(𝑖) + 𝛽 ∗ 𝑥𝑖 , 𝜏𝑅𝐸2 )

Furthermore, the following prior distributions were used:

𝜇𝑖~𝑁(0,1000)

𝑑𝑔~𝑁(0,1000)

𝛽~𝑁(0,1000)

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11 | 42

𝜏𝑡𝑖𝑚𝑒~ 𝒰(0,50)

𝜏𝑅𝐸−2 ~ 𝒢(0.1,0.1)Ι(0,2000)

We note a few points with respect to this model:

𝜃𝑖𝑡𝑘 is the estimated standardized effect of arm 𝑘 in study 𝑖 at timepoint 𝑡

𝜏𝑡𝑖𝑚𝑒2 is the variance determining how close two adjacent 𝜃𝑖𝑡𝑘 are to each other

𝜇𝑖 is the baseline effect at time 1 in study 𝑖

𝛿𝑖𝑘 is the treatment effect at time 1 in study 𝑖

𝑑𝑔 is the overall (treatment) effect at timepoint 1

An adjustment for multi-arm trials based on the approach proposed by Cooper et al1

was done (for simplification, this is omitted in the model formulation above)

One advantage of such (or any similar model) is that it can handle incomplete data structures,

i.e. also in studies for which outcome data was reported only for some of the timepoints of

interest. In a Bayesian framework, such an incomplete data structure does not pose a problem.

For example, studies which only reported outcomes at later timepoints (t > 1) will contribute less

to the estimated treatment effect 𝑑𝑔 since they are naturally down-weighted through the random

walk structure.

One challenge with such a model is that the estimated overall treatment effects corresponds to

timepoint 1. Thus, we performed our main analysis using timepoint 4 as the reference timepoint

and performed a sensitivity analysis with timepoint 1 as the reference (data not shown).

Timepoint 4 was selected because a) it was the timepoint where most trials reported data and b)

it was considered more clinically relevant than timepoint 1.

WinBUGS code used for random walk model (timepoint 4 as reference i.e. main analysis)1: model {

for(i in 1:nobs){

prec.t[i,4] <- pow(set[i,4],-2)

sefft[i,4] ~ dnorm(mean.t[i,4],prec.t[i,4])

mean.t[i,4] <- mu[trial[i]] + delta[trial[i],arm[i]]

rest[i,4] <- (mean.t[i,4] - sefft[i,4])

for(k in 1:3){

prec.t[i,k] <- pow(set[i,k],-2)

sefft[i,k] ~ dnorm(mean.t[i,k],prec.t[i,k])

mean.t[i,k] <- mu[trial[i]] + delta[trial[i],arm[i]]

rest[i,k] <- (mean.t[i,k] - sefft[i,k])

1 The code includes two additional priors for the between-trial heterogeneity that we used to assess sensitivity

of the analysis to the prior distribution. These additional analyses showed similar results.

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12 | 42

}

for(k in 5:n.times){



mean.t[i,k] ~ dnorm(mean.t[i,k-1],prec.RW[k])


}

}

for (j in 1:ns){

mu[j] ~ dnorm(0,0.001)

delta[j,1] <- 0

w[j,1] <- 0

for (k in 2:na[j]){

delta[j,k] ~ dnorm(md[j,k],tau[j,k])

md[j,k] <- d[t.s[j,k]] - d[t.s[j,1]] + sw[j,k] + beta*x[j]

tau[j,k] <- 2*tau.re*(k-1)/k

w[j,k] <- delta[j,k] - (d[t.s[j,k]] - d[t.s[j,1]] + beta*x[j])

sw[j,k] <- sum(w[j,1:(k-1)])/(k-1)

}

}

d[1] <- 0

for (k in 2:nt){

d[k] ~ dnorm(0,0.001)

}

beta ~ dnorm(0,0.001)

prec.RW[1] <- 0

indep[1] <- 0

for(i in 2:n.times){

sd.RW[i] <- pow(prec.RW[i], -0.5)

prec.RW[i] <- prec.RW.which[i,indep[i]]

prec.RW.which[i,1] <- prec.RW.common

prec.RW.which[i,2] <- pow(sd.RW.indep[i], -2)

sd.RW.indep[i] ~ dunif(0, 50)

}

prec.RW.common <- pow(sd.RW.common, -2)

sd.RW.common ~ dunif(0,50)

tau.re ~ dgamma(0.1,0.1)I(0,2000)

# Alternative prior: half normal

# tau.re <- pow(sd.re,-0.5)

# sd.re ~ dnorm(0,1)I(0,)

# Alternative prior: Rhodes 20152, drug versus placebo

# tau.re ~ dgamma(0.21,0.0003) #

}

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13 | 42

In order to model a treatment specific dose-response relationship, for each preparation the

maximum approved dose in Switzerland for the indication “osteoarthritis” was defined as the

reference dose. The treatment effect d is with respect to this reference dose, and for the

remaining doses of the preparation, the respective treatment effect was defined as

𝑑𝑔′ = 𝛾𝑔 ∗ log (

𝑑𝑜𝑠𝑒′

𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑑𝑜𝑠𝑒)

Non-informative prior distributions (normal with mean 0 and variance 1000) were given for the

parameters 𝛾𝑔 .

Alternative model 1: Full exchangeability

This model has been previously used in a network meta-analysis investigating the effectiveness

of food supplements for the treatment of osteoarthritic pain.3 It makes a relatively strong

assumption on full exchangeability in the sense that any variation between timepoints is thought

to be random around study-arm-specific standardized mean effects in the trial. In other words, it

assumes that observations over time randomly vary around an average study-arm specific

reduction.

In mathematical terms:

𝑠𝑒𝑓𝑓𝑖𝑡𝑘| 𝜃𝑖𝑡𝑘, 𝑠𝑒𝑖𝑡𝑘~ 𝒩(𝜃𝑖𝑡𝑘 , 𝑠𝑒𝑖𝑡𝑘2 )

𝜃𝑖𝑡𝑘|𝜇𝑖, 𝛿𝑖𝑘 , 𝜏𝑣 ~𝒩(𝜇𝑖 + 𝛿𝑖𝑘 , 𝜏𝑣2)

𝛿𝑖𝑘|𝑑𝑡𝑟𝑡(𝑖,𝑘), 𝑑𝑏𝑠𝑙(𝑖), 𝜏𝑅𝐸 {= 0 𝑖𝑓 𝑡𝑟𝑡(𝑖, 𝑘) = 𝑏𝑠𝑙(𝑖)

~𝒩(𝑑𝑡𝑟𝑡(𝑖,𝑘) − 𝑑𝑏𝑠𝑙(𝑖) + 𝛽 ∗ x𝑖, 𝜏𝑅𝐸2 ) 𝑒𝑙𝑠𝑒


𝜇𝑖~𝒩(0,1000)

𝑑𝑔~𝑁(0,1000)

𝜏𝜈~ 𝒰(0,50)

𝜏𝑅𝐸−2 ~ 𝒢(0.001,0.001)Ι(0,2000)

We note a few points:

𝜃𝑖𝑡𝑘 is the estimated standardized effect of arm 𝑘 in study 𝑖 at timepoint 𝑡

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14 | 42

𝜏𝜈 is the variance determining how close the arm-specific standardized effects are

distributed around the overall arm-specific standardized effect

𝜇𝑖 is the average standardized baseline effect in study 𝑖

𝛿𝑖𝑘 is the average treatment effect in study 𝑖

𝑑𝑔 is the overall (treatment) effect



𝜏𝜈~ 𝒰(0,50)

𝜏𝑅𝐸−2 ~ 𝒢(0.001,0.001)Ι(0,2000)

WinBUGS code used for full exchangeability model: model {

for(i in 1:nobs){

for (k in 1:na[trial[i]]){

prec.y[i,k] <- pow(se[i,k],-2)

seff[i,k] ~ dnorm(mean.y[i,k],prec.y[i,k])

res[i,k] <- (mean.y[i,k] - seff[i,k])

mean.y[i,k] ~ dnorm(nu[i,k],prec.nu[i,k])

prec.nu[i,k] <- pow(sd.nu[i,k],-2)

sd.nu[i,k] <- sd.rm[time[i]]

nu[i,k] <- mu[trial[i]] + delta[trial[i],k]

}

}

for (j in 1:ns){

mu[j] ~ dnorm(0,0.001)

delta[j,1] <- 0

w[j,1] <- 0

for (k in 2:na[j]){

delta[j,k] ~ dnorm(md[j,k],tau[j,k])



w[j,k] <- delta[j,k] - (d[t.s[j,k]] - d[t.s[j,1]] + beta*x[j])

sw[j,k] <- sum(w[j,1:(k-1)])/(k-1)

}

}

d[1] <- 0

for (k in 2:nt){

d[k] ~ dnorm(0,0.001)

}


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15 | 42

for(m in 1:7){

sd.rm[m] <- pow(prec.RM[m], -0.5)

prec.RM[m] <- prec.RM.which[m,indep[m]]

prec.RM.which[m,1] <- prec.RM.common

prec.RM.which[m,2] <- pow(sd.RM.indep[m],-2)

sd.RM.indep[m] ~ dunif(0,50)

}

prec.RM.common <- pow(sd.RM.common, -2)

sd.RM.common ~ dunif(0,50)

tau.re ~ dgamma(0.001,0.001)I(,2000)

sd.re <- pow(tau.re,-0.5)

}

Alternative model 2: Unconstrained baseline, random-walk on treatment effect

Following Dakin et al it may be desirable to relax assumptions further by using unconstrained baselines.4

At the same time, a random walk on the treatment effect may offer a sensible way to capture potential

temporal patterns. This model may thus be described as follows:

𝑠𝑒𝑓𝑓𝑖𝑡𝑘| 𝜇𝑖𝑡 , 𝛿𝑖𝑡𝑘 , 𝑠𝑒𝑖𝑡𝑘~ 𝒩(𝜇𝑖𝑡 + 𝛿𝑖𝑡𝑘 , 𝑠𝑒𝑖𝑡𝑘2 )

𝑓𝑜𝑟 𝑡 > 1

𝛿𝑖𝑡𝑘|𝛿𝑖(𝑡−1)𝑘, 𝜏𝑡𝑖𝑚𝑒 {= 0 𝑖𝑓 𝑡𝑟𝑡(𝑖, 𝑘) = 𝑏𝑠𝑙(𝑖)

~𝒩(𝛿𝑖(𝑡−1)𝑘, 𝜏𝑡𝑖𝑚𝑒2 ) 𝑒𝑙𝑠𝑒

𝛿𝑖1𝑘|𝑑𝑡𝑟𝑡(𝑖,𝑘), 𝑑𝑏𝑠𝑙(𝑖), 𝜏𝑅𝐸~~𝒩(𝑑𝑡𝑟𝑡(𝑖,𝑘) − 𝑑𝑏𝑠𝑙(𝑖) + 𝛽 ∗ x𝑖, 𝜏𝑅𝐸2 )


𝜇𝑖𝑡~𝑁(0,1000)

𝑑𝑔~𝑁(0,1000)

𝛽~𝑁(0,1000)

𝜏𝑡𝑖𝑚𝑒~ 𝒰(0,50)

𝜏𝑅𝐸−2 ~ 𝒢(0.001,0.001)Ι(0,2000)

We note a few points with respect to this model:

𝜏𝑡𝑖𝑚𝑒2 is the variance determining how close two adjacent 𝛿𝑖𝑡𝑘 are to each other

𝜇𝑖𝑡 is the baseline effect at time 𝑡 in study 𝑖

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16 | 42

𝛿𝑖𝑡𝑘 is the treatment effect at time 𝑡 in study 𝑖

𝑑𝑔 is the overall (treatment) effect at timepoint 1



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17 | 42

WinBUGS code used for unconstrained baseline, random walk on treatment effect model:

model {

for(i in 1:nobs){

prec.t[i,1] <- pow(set[i,1],-2)

sefft[i,1] ~ dnorm(mean.t[i,1],prec.t[i,1])

mean.t[i,1] <- mu[trial[i],1] + delta[trial[i],arm[i],1]

rest[i,1] <- (mean.t[i,1] - sefft[i,1])

for(k in 2:n.times){



mean.t[i,k] <- mu[trial[i],k] + delta[trial[i],arm[i],k]


}

}

for (j in 1:ns){

for(m in 1:n.times){

mu[j,m] ~ dnorm(0,0.0001)

delta[j,1,m] <- 0

}

w[j,1] <- 0

for (k in 2:na[j]){

delta[j,k,1] ~ dnorm(md[j,k],tau[j,k])



w[j,k] <- delta[j,k,1] - (d[t.s[j,k]] - d[t.s[j,1]] + beta*x[j])

sw[j,k] <- sum(w[j,1:(k-1)])/(k-1)

for(g in 2:n.times){

delta[j,k,g] ~ dnorm(delta[j,k,g-1],prec.RW[g])

}

}

}

d[1] <- 0

for (k in 2:nt){

d[k] ~ dnorm(0,0.001)

}


prec.RW[1] <- 0

indep[1] <- 0

for(i in 2:n.times){

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18 | 42

sd.RW[i] <- pow(prec.RW[i], -0.5)

prec.RW[i] <- prec.RW.which[i,indep[i]]

prec.RW.which[i,1] <- prec.RW.common

prec.RW.which[i,2] <- pow(sd.RW.indep[i], -2)

sd.RW.indep[i] ~ dunif(0, 50)

}

prec.RW.common <- pow(sd.RW.common, -2)

sd.RW.common ~ dunif(0,50)

tau.re ~ dgamma(0.001,0.001)I(,2000)

sd.re <- pow(tau.re,-0.5)

}

References

1. Cooper NJ, Sutton AJ, Lu G, Khunti K. Mixed comparison of stroke prevention treatments in individuals with nonrheumatic atrial fibrillation. Archives of internal medicine. 2006;166(12):1269-1275.

2. Rhodes KM, Turner RM, Higgins JP. Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data. J Clin Epidemiol. 2015;68(1):52-60.

3. Wandel S, Juni P, Tendal B, et al. Effects of glucosamine, chondroitin, or placebo in patients with osteoarthritis of hip or knee: network meta-analysis. BMJ. 2010;341:c4675.

4. Dakin HA, Welton NJ, Ades AE, Collins S, Orme M, Kelly S. Mixed treatment comparison of repeated measurements of a continuous endpoint: an example using topical treatments for primary open-angle glaucoma and ocular hypertension. Stat Med. 2011.

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19 | 42

Web-appendix 3. Definitions used to classify trials according to

components of methodological quality

Concealment of allocation

We considered allocation concealment adequate if the investigators responsible for

patient selection were unable to suspect before allocation which treatment was next.

Methods considered adequate included central randomization, coded drugs, and

sequentially numbered, sealed, opaque envelopes. Concealment was downgraded to

inadequate, if there was evidence of inadequate sequence generation.

Blinding of patients

We considered blinding of patients adequate if a trial was placebo-controlled, if

interventions were described as indistinguishable, or if double-dummy technique was

used.

Blinding of therapist

We considered blinding of therapists adequate if it was explicitly mentioned in the text

that therapists were blinded.

Completeness of outcome data

We considered statistical analyses to be adequate if all randomised patients were

included in the analysis according to the intention-to-treat principle.

Web-appendix 4. Flow diagram and references of included trials

Web-appendix Figure 1. Flow diagram of systematic review

43 potentially eligible reports retrieved for

more detailed assessment

2863 potentially relevant references

identified from Jan 2009 to Feb 2015

CENTRAL = 511

MEDLINE = 668

EMBASE = 1684

24 reports excluded based on full-text

screening

• No knee or hip OA = 1

• No RCT = 1

• Editorial, comment, review = 0

• Less then 100 Patients per trial arm = 2

• No NSAID = 2

• No Control of interest = 2

• No NSAID included in our Review = 0

• Topical NSAID = 0

• No outcome measure of interest = 7

• Post-operative pain = 1

• Multiple report from included trial = 8

2821 reports excluded from further review


• No RCT = 358


• Less then 100 Patients per trial arm = 21

• No NSAID = 591





• Duplicates = 815

19 Trials included

598 potentially eligible reports retrieved for


5389 potentially relevant references

identified from inception to December 2010

CENTRAL = 5389

548 reports excluded based on full-text

screening


• No RCT = 26


• Less than 100 patients per trial arm = 203

• No NSAID = 0







• Insufficient data = 5

• Non-English = 171

• Abstract = 40

• Other = 1

4791 reports excluded from further review


• No RCT = 275



• No NSAID = 414




• Published before 1980 = 328


• Non-English = 56

• Abstract = 12

50 trials included

74 trials included in main analysis

721 potentially relevant trials identified in

internal database

(1275 comparisons)

281 potentially eligible trials retrieved for


(542 comparisons)

440 trials excluded from further review

(733 comparisons)

234 trials excluded (418 comparisons)


• No RCT = 0


• No NSAID = 39








• Insufficient data = 24

• Non-English = 0

• Abstract = 24

• Other = 747 eligible trials retrieved for more

detailed assessment

(124 comparisons)

5 unpublished trials included

(5 comparisons)

42 trials excluded (119 comparisons)

• Duplicates = 119

References to included trials

Altman et al (2007)1, Baerwald et al (2010)2, Bensen et al (1999)3, Bin et al (2007)4, Bingham et al (2007)5, Bingham et al (2007a)5, Birbara et al (2006)6, Birbara et al (2006a)6, Bocanegra et al (1998)7, Boureau et al (2004)8, Cannon et al (2000)9, Caruso et al (1987)10, Conaghan et al (2013)11, Dahlberg et al (2009)12, Day et al (2000)13, DeLemos et al (2011)14, Doherty et al (2011)15, Ehrich et al (2001)16, Emery et al (2008)17, Essex et al (2012)18, Essex et al (2012a)19, Essex et al (2014)20, Fleischmann et al (2006)21, GAIT (2006)22-24, Gibofsky et al (2003)25, Gibofsky et al (2014)26, Gottesdiener et al (2002)27, Hawkey et al (2000)28, Herrero-Beaumont et al (2007)29, Hochberg et al (2011)30, Hochberg et al (2011a)30, Karlsson et al (2009)31, Kivitz et al (2001)32, Kivitz et al (2002)33, Kivitz et al (2004)34, Lehmann et al (2005)35, Leung et al (2002)36, Lohmander et al (2005)37, Makarowski et al (2002)38, McKenna et al (2001)39, Miceli-Richard et al (2004)40, Novartis (2005)41, Novartis (2005a)42, Novartis (2006)43, Novartis (2006a)44, Novartis (2007)45, PACES (2004)46, PACESa (2004)46, Prior et al (2014)47, Puopolo et al (2007)48, Reginster et al (2007)49, Rother et al (2007)50, Saag et al (2000)51, Saag et al (2000a)51, Schnitzer et al (2004)52, Schnitzer et al (2005)53, Schnitzer et al (2005a)54, Schnitzer et al (2009)55, Schnitzer et al (2010)56, Schnitzer et al (2011)57, Schnitzer et al (2011a)58, Sheldon et al (2005)59, Smugar et al (2006)60, Smugar et al (2006a)60, Sowers et al (2005)61, Tannenbaum et al (2004)62, Weaver et al (2006)63, Wiesenhutter et al (2005)64, Williams et al (2000)65, Williams et al (2001)66, Wittenberg et al (2006)67, Yoo et al (2014)68, Zacher et al (2003)69, Zhao et al (1999)70.

1. Altman RD, Zinsenheim JR, Temple AR, Schweinle JE. Three-month efficacy and safety of acetaminophen extended-release for osteoarthritis pain of the hip or knee: a randomized, double-blind, placebo-controlled study. Osteoarthritis and cartilage / OARS, Osteoarthritis Research Society, 2007. http://www.mrw.interscience.wiley.com/cochrane/clcentral/articles/291/CN-00586291/frame.html Journal Article; Randomized Controlled Trial . 2. Baerwald C, Verdecchia P, Duquesroix B, Frayssinet H, Ferreira T. Efficacy, safety, and effects on blood pressure of naproxcinod 750 mg twice daily compared with placebo and naproxen 500 mg twice daily in patients with osteoarthritis of the hip: a randomized, double-blind, parallel-group, multicenter study. Arthritis and rheumatism, 2010. http://onlinelibrary.wiley.com/o/cochrane/clcentral/articles/856/CN-00777856/frame.html . 3. Bensen WG, Fiechtner JJ, McMillen JI, et al. Treatment of osteoarthritis with celecoxib, a cyclooxygenase-2 inhibitor: a randomized controlled trial. Mayo Clinic proceedings Mayo Clinic, 1999. http://www.mrw.interscience.wiley.com/cochrane/clcentral/articles/056/CN-00264056/frame.html . 4. Bin SI, Wu SS, Zeng X, Moore A, Frank N. Efficacy of lumiracoxib in relieving pain associated with knee osteoarthritis: A 6-week, randomized, double-blind, parallel-group study. APLAR Journal of Rheumatology, 2007. http://www.mrw.interscience.wiley.com/cochrane/clcentral/articles/938/CN-00641938/frame.html . 5. Bingham CO, Sebba AI, Rubin BR, et al. Efficacy and safety of etoricoxib 30 mg and celecoxib 200 mg in the treatment of osteoarthritis in two identically designed, randomized, placebo-controlled, non-inferiority studies. Rheumatology (Oxford, England), 2007. http://www.mrw.interscience.wiley.com/cochrane/clcentral/articles/419/CN-00576419/frame.html . 6. Birbara C, Ruoff G, Sheldon E, et al. Efficacy and safety of rofecoxib 12.5 mg and celecoxib 200 mg in two similarly designed osteoarthritis studies. Current medical research and opinion, 2006. http://www.mrw.interscience.wiley.com/cochrane/clcentral/articles/019/CN-00554019/frame.html . 7. Bocanegra TS, Weaver AL, Tindall EA, et al. Diclofenac/misoprostol compared with diclofenac in the treatment of osteoarthritis of the knee or hip: a randomized, placebo controlled trial. Arthrotec Osteoarthritis Study Group. J Rheumatol 1998; 25(8): 1602-11. 8. Boureau F, Schneid H, Zeghari N, Wall R, Bourgeois P. The IPSO study: ibuprofen, paracetamol study in osteoarthritis. A randomised comparative clinical study comparing the efficacy and safety of ibuprofen and paracetamol analgesic treatment of osteoarthritis of the knee or hip. Annals of the Rheumatic Diseases, 2004. http://www.mrw.interscience.wiley.com/cochrane/clcentral/articles/958/CN-00480958/frame.html . 9. Cannon GW, Caldwell JR, Holt P, et al. Rofecoxib, a specific inhibitor of cyclooxygenase 2, with clinical efficacy comparable with that of diclofenac sodium: Results of a one-year, randomized, clinical trial in patients with osteoarthritis of the knee and hip. Arthritis & Rheumatism, 2000. http://www.mrw.interscience.wiley.com/cochrane/clcentral/articles/080/CN-00424080/frame.html .

http://www.mrw.interscience.wiley.com/cochrane/clcentral/articles/291/CN-00586291/frame.html

http://onlinelibrary.wiley.com/o/cochrane/clcentral/articles/856/CN-00777856/frame.html










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https://www.novctrd.com/CtrdWeb/displaypdf.nov?trialresultid=1559
















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Web-appendix 5. Treatment effect according to time point Web-appendix Figure 2. Graphical display of available information according to timepoint. The

size of the circle is proportional to the statistical information available for a specified preparation

at a specified timepoint across trials (precision).

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Web-appendix Table 1. Treatment effect according to time point. Data displayed are effect

sizes and 95% credibility intervals

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28 | 42

*Maximum daily dosage. We included in our analysis data at 1, 2, 4, 6, 12, 26 weeks after end of

treatment. There was not sufficient data to perform an analysis for week 52. Please note that the

estimates come from separate network meta-analyses per timepoint and do not take into account the

correlated nature of the data. The credibility intervals may therefore be too narrow. Colors correspond to

the estimated effect size per timepoint with the following color code:

Effect size -0.72 -0.37 0 0.37

Color code

Web-appendix 6. Assessment of Model Fit

Web-appendix Table 2 Assessment of model fit

Outcome Data points DIC Residuals Q-Q plots

Number (%)

within 1·96 SND

All trials

Pain 473 -917.6 469 (99.2%) Adequate

Physical function 303 -559.9 303 (100%) Adequate

DIC: Deviance information criterion; SND: Standard normal distribution

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30 | 42

Web-appendix 7. Inconsistency Inconsistency was assessed based on the main model which used timepoint 6 as reference

timepont

Pain outcome

The median inconsistency factor (ICF) for pain was 0.06 and it ranged from 0.00 to 0.42. Four

out of 69 ICFs were statistically significant. None of the ICFs for pain were clinically relevant.

Web-appendix Figure 2. Inconsistency factors for pain outcome analysis. CI: confidence

interval. RoM: Ratio of means. 01 = Placebo; 02 =Paracetamol <2000mg; 03 = Paracetamol

3000mg; 04 = Paracetamol 3900-4000mg; 05 = Rofecoxib 12.5mg; 06 = Rofecoxib 25mg; 07

=Rofecoxib 50mg; 08 = Lumiracoxib 100mg; 09 = Lumiracoxib 200mg; 10 = Lumiracoxib

400mg; 11 = Etoricoxib 30mg; 12 = Etoricoxib 60mg; 13 = Etoricoxib 90mg; 14 = Diclofenac

70mg; 15 = Diclofenac 100mg; 16= Diclofenac 150mg; 17 = Celecoxib 100mg; 18 = Celecoxib

200mg; 19 = Celecoxib 400mg; 20 = Naproxen 750mg; 21 = Naproxen 1000mg; 22 = Ibuprofen

1200mg; 23 = Ibuprofen 2400mg

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31 | 42

10-18-21

01-12-21

11-12-18-21

01-09-18

12-16-18-21

01-12-13

05-07-23

01-06-23

01-06-18

08-09-18

06-12-16-21

10-18-19

06-07-23

01-06-07

01-08-18

09-10-18

01-09-10

01-10-18

05-06-07

01-05-07

06-11-18-23

01-11-18

05-11-18-23

09-18-19

01-05-18

01-11-12

18-19-21

01-11-13

01-06-21

01-02-04

01-08-09

01-18-21

01-18-19

01-10-21

17-18-21

10-19-21

01-05-23

01-04-18

01-17-18

05-06-16

05-16-18

01-04-05

01-11-23

05-06-18

01-19-21

04-05-06

06-18-21

01-06-16

06-16-18

01-04-06

04-05-18

01-17-21

11-12-16-18

01-17-19

09-10-19

01-09-19

01-10-19

01-05-06

01-16-18

04-06-18

05-06-23

01-15-18

01-05-16

01-12-16

17-19-21

17-18-19

01-07-23

01-14-15

11-12-13

Loop

0.18 (0.06, 0.31)

0.30 (0.07, 0.52)

0.40 (0.08, 0.72)

0.12 (0.01, 0.24)

0.31 (-0.01, 0.63)

0.42 (-0.02, 0.86)

0.32 (-0.03, 0.67)

0.24 (-0.04, 0.51)

0.13 (-0.03, 0.30)

0.08 (-0.03, 0.20)

0.24 (-0.13, 0.62)

0.15 (-0.08, 0.38)

0.19 (-0.12, 0.50)

0.33 (-0.21, 0.87)

0.07 (-0.05, 0.19)

0.07 (-0.06, 0.21)

0.07 (-0.07, 0.22)

0.10 (-0.10, 0.30)

0.15 (-0.16, 0.47)

0.33 (-0.36, 1.03)

0.11 (-0.13, 0.34)

0.09 (-0.11, 0.30)

0.11 (-0.14, 0.35)

0.12 (-0.17, 0.42)

0.07 (-0.10, 0.23)

0.15 (-0.24, 0.54)

0.07 (-0.11, 0.26)

0.17 (-0.26, 0.60)

0.12 (-0.20, 0.44)

0.15 (-0.26, 0.56)

0.05 (-0.09, 0.18)

0.04 (-0.09, 0.18)

0.06 (-0.13, 0.25)

0.05 (-0.11, 0.21)

0.06 (-0.14, 0.27)

0.07 (-0.15, 0.28)

0.10 (-0.23, 0.43)

0.05 (-0.14, 0.25)

0.05 (-0.17, 0.27)

0.05 (-0.18, 0.29)

0.08 (-0.27, 0.43)

0.06 (-0.22, 0.34)

0.07 (-0.24, 0.37)

0.03 (-0.12, 0.17)

0.03 (-0.14, 0.20)

0.03 (-0.18, 0.24)

0.03 (-0.20, 0.26)

0.07 (-0.40, 0.53)

0.04 (-0.27, 0.36)

0.04 (-0.31, 0.40)

0.03 (-0.21, 0.27)

0.03 (-0.21, 0.26)

0.06 (-0.51, 0.63)

0.03 (-0.27, 0.32)

0.03 (-0.31, 0.37)

0.02 (-0.27, 0.32)

0.01 (-0.22, 0.24)

0.01 (-0.24, 0.27)

0.01 (-0.31, 0.33)

0.00 (-0.22, 0.23)

0.00 (-0.20, 0.20)

0.00 (-0.40, 0.41)

0.00 (-0.35, 0.35)

0.00 (-0.51, 0.51)

0.00 (-0.24, 0.24)

0.00 (-0.18, 0.18)

0.00 (-0.78, 0.78)

0.00 (-0.48, 0.48)

0.00 (-0.46, 0.46)

IF (95% CI)

1.199

1.347

1.489

1.131

1.368

1.517

1.379

1.266

1.142

1.087

1.277

1.162

1.209

1.394

1.072

1.077

1.077

1.106

1.167

1.395

1.113

1.097

1.114

1.133

1.067

1.165

1.075

1.182

1.128

1.167

1.050

1.045

1.061

1.050

1.066

1.068

1.105

1.055

1.053

1.057

1.083

1.063

1.068

1.026

1.028

1.033

1.035

1.071

1.042

1.045

1.028

1.026

1.059

1.029

1.027

1.023

1.012

1.012

1.012

1.005

1.002

1.003

1.002

1.001

1.000

1.000

1.000

1.000

1.000

RoR

0.18 (0.06, 0.31)

0.30 (0.07, 0.52)

0.40 (0.08, 0.72)

0.12 (0.01, 0.24)

0.31 (-0.01, 0.63)

0.42 (-0.02, 0.86)

0.32 (-0.03, 0.67)

0.24 (-0.04, 0.51)

0.13 (-0.03, 0.30)

0.08 (-0.03, 0.20)

0.24 (-0.13, 0.62)

0.15 (-0.08, 0.38)

0.19 (-0.12, 0.50)

0.33 (-0.21, 0.87)

0.07 (-0.05, 0.19)

0.07 (-0.06, 0.21)

0.07 (-0.07, 0.22)

0.10 (-0.10, 0.30)

0.15 (-0.16, 0.47)

0.33 (-0.36, 1.03)

0.11 (-0.13, 0.34)

0.09 (-0.11, 0.30)

0.11 (-0.14, 0.35)

0.12 (-0.17, 0.42)

0.07 (-0.10, 0.23)

0.15 (-0.24, 0.54)

0.07 (-0.11, 0.26)

0.17 (-0.26, 0.60)

0.12 (-0.20, 0.44)

0.15 (-0.26, 0.56)

0.05 (-0.09, 0.18)

0.04 (-0.09, 0.18)

0.06 (-0.13, 0.25)

0.05 (-0.11, 0.21)

0.06 (-0.14, 0.27)

0.07 (-0.15, 0.28)

0.10 (-0.23, 0.43)

0.05 (-0.14, 0.25)

0.05 (-0.17, 0.27)

0.05 (-0.18, 0.29)

0.08 (-0.27, 0.43)

0.06 (-0.22, 0.34)

0.07 (-0.24, 0.37)

0.03 (-0.12, 0.17)

0.03 (-0.14, 0.20)

0.03 (-0.18, 0.24)

0.03 (-0.20, 0.26)

0.07 (-0.40, 0.53)

0.04 (-0.27, 0.36)

0.04 (-0.31, 0.40)

0.03 (-0.21, 0.27)

0.03 (-0.21, 0.26)

0.06 (-0.51, 0.63)

0.03 (-0.27, 0.32)

0.03 (-0.31, 0.37)

0.02 (-0.27, 0.32)

0.01 (-0.22, 0.24)

0.01 (-0.24, 0.27)

0.01 (-0.31, 0.33)

0.00 (-0.22, 0.23)

0.00 (-0.20, 0.20)

0.00 (-0.40, 0.41)

0.00 (-0.35, 0.35)

0.00 (-0.51, 0.51)

0.00 (-0.24, 0.24)

0.00 (-0.18, 0.18)

0.00 (-0.78, 0.78)

0.00 (-0.48, 0.48)

0.00 (-0.46, 0.46)

1.199

1.347

1.489

1.131

1.368

1.517

1.379

1.266

1.142

1.087

1.277

1.162

1.209

1.394

1.072

1.077

1.077

1.106

1.167

1.395

1.113

1.097

1.114

1.133

1.067

1.165

1.075

1.182

1.128

1.167

1.050

1.045

1.061

1.050

1.066

1.068

1.105

1.055

1.053

1.057

1.083

1.063

1.068

1.026

1.028

1.033

1.035

1.071

1.042

1.045

1.028

1.026

1.059

1.029

1.027

1.023

1.012

1.012

1.012

1.005

1.002

1.003

1.002

1.001

1.000

1.000

1.000

1.000

1.000

0-1 -.8 -.5 -.2 0 .2 .5 .8 1

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32 | 42

Function outcome

The median inconsistency factor (ICF) for function was 0.07 and it ranged from 0.00 to 0.57.

Three out of 57 ICFs were statistically significant. None of ICFs for function were clinically

relevant.

Web-appendix Figure 3. Inconsistency factors for physical function outcome analysis. CI:

confidence interval. RoM: Ratio of means. 01 = Placebo; 02 =Paracetamol <2000mg; 03 =

Paracetamol 3000mg; 04 = Paracetamol 3900-4000mg; 05 = Rofecoxib 12.5mg; 06 = Rofecoxib

25mg; 07 =Rofecoxib 50mg; 08 = Lumiracoxib 100mg; 09 = Lumiracoxib 200mg; 10 =

Lumiracoxib 400mg; 11 = Etoricoxib 30mg; 12 = Etoricoxib 60mg; 13 = Diclofenac 70mg; 14 =

Diclofenac 100mg; 15= Diclofenac 150mg; 16 = Celecoxib 100mg; 17 = Celecoxib 200mg; 18 =

Celecoxib 400mg; 19 = Naproxen 750mg; 20 = Naproxen 1000mg; 21 = Ibuprofen 1200mg; 22

= Ibuprofen 2400mg

WEB APPENDIX



33 | 42

01-06-07

01-07-22

12-15-17-20

01-06-17

01-16-20

06-12-15-20

10-18-20

01-18-20

01-06-22

01-12-15

01-09-10

01-12-20

01-08-17

01-02-04

10-17-18

01-06-20

04-05-06

01-11-17

06-17-20

05-06-15

01-17-20

09-10-17

01-14-17

10-17-20

01-08-09

01-11-22

01-15-17

17-18-20

01-10-17

01-10-18

04-06-17

01-05-15

01-04-17

06-07-22

16-17-20

06-15-17

01-05-22

05-11-17-22

01-09-17

01-10-20

05-06-17

06-11-17-22

01-05-06

08-09-17

01-16-17

04-05-17

01-06-15

01-16-18

01-05-17

05-06-22

16-17-18

01-04-06

01-17-18

05-15-17

16-18-20

01-04-05

01-13-14

Loop

0.57 (0.19, 0.94)

0.38 (0.07, 0.69)

0.31 (0.01, 0.60)

0.14 (-0.00, 0.29)

0.15 (-0.01, 0.31)

0.28 (-0.02, 0.57)

0.25 (-0.02, 0.53)

0.14 (-0.02, 0.30)

0.23 (-0.04, 0.49)

0.21 (-0.08, 0.49)

0.18 (-0.08, 0.43)

0.16 (-0.08, 0.40)

0.07 (-0.04, 0.18)

0.19 (-0.14, 0.52)

0.16 (-0.13, 0.45)

0.16 (-0.14, 0.46)

0.11 (-0.10, 0.32)

0.10 (-0.10, 0.29)

0.10 (-0.10, 0.30)

0.13 (-0.13, 0.40)

0.05 (-0.07, 0.18)

0.07 (-0.09, 0.22)

0.17 (-0.22, 0.56)

0.06 (-0.08, 0.19)

0.09 (-0.12, 0.29)

0.12 (-0.19, 0.43)

0.12 (-0.19, 0.42)

0.07 (-0.12, 0.25)

0.07 (-0.12, 0.26)

0.10 (-0.19, 0.39)

0.06 (-0.12, 0.24)

0.13 (-0.27, 0.52)

0.05 (-0.11, 0.22)

0.10 (-0.21, 0.41)

0.05 (-0.12, 0.22)

0.07 (-0.18, 0.32)

0.11 (-0.27, 0.48)

0.07 (-0.18, 0.32)

0.03 (-0.09, 0.15)

0.06 (-0.17, 0.28)

0.04 (-0.13, 0.20)

0.04 (-0.19, 0.28)

0.05 (-0.25, 0.36)

0.02 (-0.10, 0.13)

0.02 (-0.14, 0.19)

0.03 (-0.19, 0.25)

0.06 (-0.40, 0.52)

0.03 (-0.23, 0.30)

0.02 (-0.15, 0.19)

0.02 (-0.21, 0.26)

0.01 (-0.16, 0.19)

0.02 (-0.32, 0.36)

0.01 (-0.16, 0.18)

0.01 (-0.26, 0.28)

0.00 (-0.17, 0.18)

0.00 (-0.30, 0.30)

0.00 (-0.48, 0.48)

IF (95% CI)

1.760

1.466

1.361

1.153

1.163

1.319

1.286

1.153

1.253

1.230

1.195

1.170

1.074

1.211

1.177

1.176

1.119

1.104

1.107

1.144

1.056

1.071

1.186

1.058

1.089

1.127

1.123

1.069

1.070

1.101

1.060

1.136

1.055

1.104

1.053

1.074

1.114

1.074

1.031

1.060

1.038

1.045

1.056

1.019

1.025

1.030

1.061

1.035

1.019

1.022

1.012

1.019

1.007

1.009

1.003

1.001

1.000

RoR

0.57 (0.19, 0.94)

0.38 (0.07, 0.69)

0.31 (0.01, 0.60)

0.14 (-0.00, 0.29)

0.15 (-0.01, 0.31)

0.28 (-0.02, 0.57)

0.25 (-0.02, 0.53)

0.14 (-0.02, 0.30)

0.23 (-0.04, 0.49)

0.21 (-0.08, 0.49)

0.18 (-0.08, 0.43)

0.16 (-0.08, 0.40)

0.07 (-0.04, 0.18)

0.19 (-0.14, 0.52)

0.16 (-0.13, 0.45)

0.16 (-0.14, 0.46)

0.11 (-0.10, 0.32)

0.10 (-0.10, 0.29)

0.10 (-0.10, 0.30)

0.13 (-0.13, 0.40)

0.05 (-0.07, 0.18)

0.07 (-0.09, 0.22)

0.17 (-0.22, 0.56)

0.06 (-0.08, 0.19)

0.09 (-0.12, 0.29)

0.12 (-0.19, 0.43)

0.12 (-0.19, 0.42)

0.07 (-0.12, 0.25)

0.07 (-0.12, 0.26)

0.10 (-0.19, 0.39)

0.06 (-0.12, 0.24)

0.13 (-0.27, 0.52)

0.05 (-0.11, 0.22)

0.10 (-0.21, 0.41)

0.05 (-0.12, 0.22)

0.07 (-0.18, 0.32)

0.11 (-0.27, 0.48)

0.07 (-0.18, 0.32)

0.03 (-0.09, 0.15)

0.06 (-0.17, 0.28)

0.04 (-0.13, 0.20)

0.04 (-0.19, 0.28)

0.05 (-0.25, 0.36)

0.02 (-0.10, 0.13)

0.02 (-0.14, 0.19)

0.03 (-0.19, 0.25)

0.06 (-0.40, 0.52)

0.03 (-0.23, 0.30)

0.02 (-0.15, 0.19)

0.02 (-0.21, 0.26)

0.01 (-0.16, 0.19)

0.02 (-0.32, 0.36)

0.01 (-0.16, 0.18)

0.01 (-0.26, 0.28)

0.00 (-0.17, 0.18)

0.00 (-0.30, 0.30)

0.00 (-0.48, 0.48)

1.760

1.466

1.361

1.153

1.163

1.319

1.286

1.153

1.253

1.230

1.195

1.170

1.074

1.211

1.177

1.176

1.119

1.104

1.107

1.144

1.056

1.071

1.186

1.058

1.089

1.127

1.123

1.069

1.070

1.101

1.060

1.136

1.055

1.104

1.053

1.074

1.114

1.074

1.031

1.060

1.038

1.045

1.056

1.019

1.025

1.030

1.061

1.035

1.019

1.022

1.012

1.019

1.007

1.009

1.003

1.001

1.000

0-1 -.8 -.5 -.2 0 .2 .5 .8 1

WEB APPENDIX



34 | 42

Web-appendix 8. Analyses using alternative models for network meta-

analysis

Web-appendix Figure 4. Analysis of primary outcome pain using the alternative model with full

exchangeability, mean effect.

CrI: Credibility interval; *Maximum daily dosage. Area between dashed lines indicates treatment

effect estimates below the minimal clinically important difference.

Paracetamol <2000mg

Paracetamol 3000mg

*Paracetamol 3900-4000mg

Rofecoxib 12.5mg

*Rofecoxib 25mg

Rofecoxib 50mg

Lumiracoxib 100mg

*Lumiracoxib 200mg

Lumiracoxib 400mg

Etoricoxib 30mg

*Etoricoxib 60mg

Etoricoxib 90mg

Diclofenac 70mg

Diclofenac 100mg

*Diclofenac 150mg

Celecoxib 100mg

Celecoxib 200mg

*Celecoxib 400mg

Naproxen 750mg

*Naproxen 1000mg

Ibuprofen 1200mg

*Ibuprofen 2400mg

-0.08 (-0.31, 0.17)

-0.16 (-0.48, 0.16)

-0.18 (-0.26, -0.10)

-0.42 (-0.48, -0.35)

-0.49 (-0.55, -0.43)

-0.51 (-0.67, -0.34)

-0.33 (-0.40, -0.26)

-0.34 (-0.40, -0.28)

-0.42 (-0.51, -0.32)

-0.49 (-0.58, -0.40)

-0.53 (-0.64, -0.42)

-0.58 (-0.85, -0.32)

-0.22 (-0.51, 0.07)

-0.30 (-0.46, -0.13)

-0.55 (-0.64, -0.46)

-0.16 (-0.27, -0.06)

-0.35 (-0.39, -0.32)

-0.34 (-0.42, -0.25)

-0.04 (-0.26, 0.18)

-0.40 (-0.46, -0.35)

-0.34 (-0.70, 0.02)

-0.42 (-0.50, -0.33)

Active treatment better Placebo better

0-1.25 -1 -.75 -.5 -.25 .25 .5

Intervention Effect size (95% CI)

WEB APPENDIX



35 | 42

Web-appendix Figure 5. Analysis of primary outcome pain using the alternative random-walk

model with unconstrained baseline estimates.

CrI: Credibility interval; *Maximum daily dosage. Area between dashed lines indicates treatment

effect estimates below the minimal clinically important difference.


Paracetamol <2000mg

Paracetamol 3000mg


Rofecoxib 12.5mg

*Rofecoxib 25mg

Rofecoxib 50mg

Lumiracoxib 100mg

*Lumiracoxib 200mg

Lumiracoxib 400mg

Etoricoxib 30mg

*Etoricoxib 60mg

Etoricoxib 90mg

Diclofenac 70mg

Diclofenac 100mg

*Diclofenac 150mg

Celecoxib 100mg

Celecoxib 200mg

*Celecoxib 400mg

Naproxen 750mg

*Naproxen 1000mg

Ibuprofen 1200mg

*Ibuprofen 2400mg

-0.08 (-0.33, 0.20)

-0.19 (-0.48, 0.11)

-0.18 (-0.26, -0.09)

-0.41 (-0.48, -0.35)

-0.49 (-0.55, -0.43)

-0.48 (-0.65, -0.31)

-0.33 (-0.40, -0.25)

-0.34 (-0.41, -0.28)

-0.42 (-0.51, -0.33)

-0.48 (-0.57, -0.39)

-0.53 (-0.64, -0.42)

-0.56 (-0.85, -0.30)

-0.14 (-0.45, 0.14)

-0.27 (-0.44, -0.06)

-0.55 (-0.64, -0.46)

-0.17 (-0.27, -0.07)

-0.35 (-0.39, -0.31)

-0.34 (-0.43, -0.25)

-0.05 (-0.27, 0.18)

-0.40 (-0.46, -0.35)

-0.37 (-0.71, -0.02)

-0.41 (-0.50, -0.32)


0-1.25 -1 -.75 -.5 -.25 .25 .5

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36 | 42

Web-appendix 9. Results of adjusted models

Web-appendix Figure 6. Analysis of primary outcome pain adjusted for concealment of

allocation.

CrI: Credibility interval; *Maximum daily dosage.

Paracetamol <2000mg

Paracetamol 3000mg


Rofecoxib 12.5mg

*Rofecoxib 25mg

Rofecoxib 50mg

Lumiracoxib 100mg

*Lumiracoxib 200mg

Lumiracoxib 400mg

Etoricoxib 30mg

*Etoricoxib 60mg

Etoricoxib 90mg

Diclofenac 70mg

Diclofenac 100mg

*Diclofenac 150mg

Celecoxib 100mg

Celecoxib 200mg

*Celecoxib 400mg

Naproxen 750mg

*Naproxen 1000mg

Ibuprofen 1200mg

*Ibuprofen 2400mg

-0.10 (-0.45, 0.24)

-0.17 (-0.66, 0.33)

-0.18 (-0.29, -0.07)

-0.46 (-0.55, -0.36)

-0.54 (-0.63, -0.44)

-0.64 (-0.88, -0.41)

-0.37 (-0.48, -0.25)

-0.36 (-0.46, -0.26)

-0.45 (-0.61, -0.30)

-0.51 (-0.64, -0.39)

-0.60 (-0.76, -0.45)

-0.63 (-0.92, -0.34)

-0.26 (-0.65, 0.13)

-0.37 (-0.62, -0.12)

-0.61 (-0.75, -0.47)

-0.19 (-0.34, -0.05)

-0.39 (-0.47, -0.32)

-0.36 (-0.50, -0.23)

-0.08 (-0.46, 0.29)

-0.43 (-0.53, -0.33)

-0.32 (-0.87, 0.23)

-0.45 (-0.58, -0.32)


0-1.25 -1 -.75 -.5 -.25 .25 .5


WEB APPENDIX



37 | 42

Web-appendix Figure 7. Analysis of primary outcome pain adjusted for blinding of therapist.


Paracetamol <2000mg

Paracetamol 3000mg


Rofecoxib 12.5mg

*Rofecoxib 25mg

Rofecoxib 50mg

Lumiracoxib 100mg

*Lumiracoxib 200mg

Lumiracoxib 400mg

Etoricoxib 30mg

*Etoricoxib 60mg

Etoricoxib 90mg

Diclofenac 70mg

Diclofenac 100mg

*Diclofenac 150mg

Celecoxib 100mg

Celecoxib 200mg

*Celecoxib 400mg

Naproxen 750mg

*Naproxen 1000mg

Ibuprofen 1200mg

*Ibuprofen 2400mg

-0.07 (-0.42, 0.27)

-0.17 (-0.66, 0.33)

-0.16 (-0.28, -0.05)

-0.43 (-0.50, -0.35)

-0.51 (-0.58, -0.43)

-0.61 (-0.84, -0.39)

-0.34 (-0.44, -0.23)

-0.34 (-0.43, -0.25)

-0.43 (-0.58, -0.28)

-0.49 (-0.61, -0.37)

-0.58 (-0.73, -0.43)

-0.62 (-0.91, -0.33)

-0.22 (-0.61, 0.17)

-0.34 (-0.58, -0.10)

-0.57 (-0.70, -0.45)

-0.16 (-0.30, -0.03)

-0.36 (-0.42, -0.31)

-0.33 (-0.46, -0.21)

-0.05 (-0.42, 0.32)

-0.40 (-0.48, -0.31)

-0.29 (-0.84, 0.25)

-0.43 (-0.54, -0.31)


-1.25 -1 -.75 -.5 -.25 0 .25 .5


WEB APPENDIX



38 | 42

Web-appendix Figure 8. Analysis of primary outcome pain adjusted for completeness of

outcome data.


Paracetamol <2000mg

Paracetamol 3000mg


Rofecoxib 12.5mg

*Rofecoxib 25mg

Rofecoxib 50mg

Lumiracoxib 100mg

*Lumiracoxib 200mg

Lumiracoxib 400mg

Etoricoxib 30mg

*Etoricoxib 60mg

Etoricoxib 90mg

Diclofenac 70mg

Diclofenac 100mg

*Diclofenac 150mg

Celecoxib 100mg

Celecoxib 200mg

*Celecoxib 400mg

Naproxen 750mg

*Naproxen 1000mg

Ibuprofen 1200mg

*Ibuprofen 2400mg

-0.08 (-0.42, 0.26)

-0.19 (-0.69, 0.31)

-0.17 (-0.28, -0.06)

-0.44 (-0.55, -0.33)

-0.52 (-0.63, -0.41)

-0.63 (-0.87, -0.39)

-0.35 (-0.48, -0.23)

-0.35 (-0.46, -0.24)

-0.44 (-0.60, -0.28)

-0.51 (-0.65, -0.37)

-0.59 (-0.75, -0.43)

-0.63 (-0.92, -0.33)

-0.23 (-0.62, 0.16)

-0.34 (-0.58, -0.10)

-0.59 (-0.74, -0.44)

-0.18 (-0.33, -0.03)

-0.38 (-0.47, -0.29)

-0.35 (-0.49, -0.20)

-0.06 (-0.45, 0.31)

-0.41 (-0.52, -0.30)

-0.33 (-0.90, 0.24)

-0.44 (-0.59, -0.30)


0-1.25 -1 -.75 -.5 -.25 .25 .5


WEB APPENDIX



39 | 42

Web-appendix Figure 9. Analysis of primary outcome pain adjusted for approach used for data

imputation.


Paracetamol <2000mg

Paracetamol 3000mg


Rofecoxib 12.5mg

*Rofecoxib 25mg

Rofecoxib 50mg

Lumiracoxib 100mg

*Lumiracoxib 200mg

Lumiracoxib 400mg

Etoricoxib 30mg

*Etoricoxib 60mg

Etoricoxib 90mg

Diclofenac 70mg

Diclofenac 100mg

*Diclofenac 150mg

Celecoxib 100mg

Celecoxib 200mg

*Celecoxib 400mg

Naproxen 750mg

*Naproxen 1000mg

Ibuprofen 1200mg

*Ibuprofen 2400mg

-0.07 (-0.41, 0.27)

-0.16 (-0.66, 0.33)

-0.16 (-0.27, -0.06)

-0.42 (-0.51, -0.33)

-0.50 (-0.59, -0.41)

-0.61 (-0.85, -0.38)

-0.34 (-0.45, -0.22)

-0.34 (-0.44, -0.24)

-0.43 (-0.58, -0.27)

-0.49 (-0.62, -0.36)

-0.58 (-0.73, -0.43)

-0.61 (-0.90, -0.32)

-0.22 (-0.60, 0.17)

-0.34 (-0.58, -0.10)

-0.57 (-0.70, -0.44)

-0.16 (-0.30, -0.02)

-0.36 (-0.43, -0.29)

-0.33 (-0.46, -0.19)

-0.05 (-0.42, 0.32)

-0.39 (-0.48, -0.30)

-0.29 (-0.85, 0.27)

-0.42 (-0.55, -0.30)


0-1.25 -1 -.75 -.5 -.25 .25 .5


WEB APPENDIX



40 | 42

Web-appendix Figure 10. Analysis of primary outcome pain adjusted for joint affected by

osteoathritis.


Paracetamol <2000mg

Paracetamol 3000mg


Rofecoxib 12.5mg

*Rofecoxib 25mg

Rofecoxib 50mg

Lumiracoxib 100mg

*Lumiracoxib 200mg

Lumiracoxib 400mg

Etoricoxib 30mg

*Etoricoxib 60mg

Etoricoxib 90mg

Diclofenac 70mg

Diclofenac 100mg

*Diclofenac 150mg

Celecoxib 100mg

Celecoxib 200mg

*Celecoxib 400mg

Naproxen 750mg

*Naproxen 1000mg

Ibuprofen 1200mg

*Ibuprofen 2400mg

-0.07 (-0.41, 0.27)

-0.16 (-0.66, 0.33)

-0.16 (-0.27, -0.06)

-0.42 (-0.51, -0.33)

-0.50 (-0.59, -0.41)

-0.61 (-0.85, -0.38)

-0.34 (-0.45, -0.22)

-0.34 (-0.44, -0.24)

-0.43 (-0.58, -0.27)

-0.49 (-0.62, -0.36)

-0.58 (-0.73, -0.43)

-0.61 (-0.90, -0.32)

-0.22 (-0.60, 0.17)

-0.34 (-0.58, -0.10)

-0.57 (-0.70, -0.44)

-0.16 (-0.30, -0.02)

-0.36 (-0.43, -0.29)

-0.33 (-0.46, -0.19)

-0.05 (-0.42, 0.32)

-0.39 (-0.48, -0.30)

-0.29 (-0.85, 0.27)

-0.42 (-0.55, -0.30)


0-1.25 -1 -.75 -.5 -.25 .25 .5


WEB APPENDIX



41 | 42

Web-appendix 10. Comparison adjusted funnel plots

Web-appendix Figure 11. Comparison adjusted funnel plot for the outcome pain.

0.1

.2S

tand

ard

err

or

of

effe

ct

siz

e

-.5 -.25 0 .25 .5Effect size centred at comparison-specific pooled effect (yiXY-µXY)

WEB APPENDIX



42 | 42

Web-appendix Figure 12. Comparison adjusted funnel plot for the outcome function.

0.1

.2S

tandard

err

or

of

effect

siz

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-.5 -.25 0 .25 .5Effect size centred at comparison-specific pooled effect (yiXY-µXY)

Protocol for a systematic review and network

meta-analysis

Clinical effectiveness of non-steroidal anti-

inflammatory drugs: a comprehensive network

meta-analysis

Authors: Trelle S, Nüesch E, Jüni P

Date: September 25, 2012 (replaces version of December 7th, 2010)

Content

Background ............................................................................................................................ 2 Objectives ............................................................................................................................... 2

Criteria for considering studies for this review ...................................................................... 3 Types of studies .................................................................................................................. 3

Types of participants .......................................................................................................... 3 Types of interventions ........................................................................................................ 3 Types of outcome measures ............................................................................................... 3

Search methods for identification of studies .......................................................................... 3 Electronic searches ............................................................................................................. 3

Searching other ressources ................................................................................................. 4 Data collection and analysis ................................................................................................... 4

Selection of studies ............................................................................................................. 4 Data extraction and management ....................................................................................... 4

Assessment of risk of bias in included studies ................................................................... 5

Measures of treatment effect .............................................................................................. 5

Data synthesis ..................................................................................................................... 6 Role of the funding source ................................................................................................. 7 References .......................................................................................................................... 7

Change history ........................................................................................................................ 9

Protocol for a systematic review and network meta-analysis Clinical effectiveness of non-steroidal anti-inflammatory drugs: a comprehensive network meta-analysis

September 25, 2012 2 | 9

Background

Pain is the most relevant symptom of osteoarthritis from a patient perspective with a major

impact on quality-of-life (1). Management of pain is based on a sequential, hierarchical

approach with non-steroidal anti-inflammatory drugs being among the most often used

interventions (2). Several systematic reviews investigated the effectiveness of these agents (3).

However, previous systematic reviews only considered direct evidence from head-to-head

comparisons and did not aim at synthesizing all available evidence. Moreover, review authors

often refrained from conducting a meta-analyses because of differences in outcome measures

reported in individual trials therefore limiting their use for informing clinical practice.

As noted in the previous section most meta-analyses consider pair-wise, direct comparisons of

interventions. This makes it often difficult to determine the best treatment. Indirect

comparisons are often needed to establish a 'ranking' (sometimes called league table) of

interventions. For example, we indirectly compare naproxen and diclofenac if naproxen is

only tested versus rofecoxib and diclofenac is only tested versus rofecoxib. Usually, these

indirect comparisons are done informally which increases the risk of bias. Suppose that

rofecoxib is better than diclofenac and naproxen is better than rofecoxib. Then it is usually

assumed that naproxen is better than diclofenac. In the field of non-steroidal anti-

inflammatory drugs, however, the situation is actually more complex with several

preparations tested (see figure below). The method of network-meta-analysis allows to

combine the available information in one meta-analysis and to rank interventions accordingly.

This approach provides estimates of treatment differences and assesses the uncertainties in

these estimates by detecting heterogeneity and inconsistencies among the trials. It is

especially useful in situations with many different interventions (4).

Objectives

The overarching aim of the project is to describe the current evidence of pharmacological pain

management for osteoarthritis. The proposed network meta-analysis will enable us:

To summarize and compare the effectiveness of non-steroidal anti-inflammatory drugs

(NSAIDs), including cox-2 selective inhibitors and acetaminophen, in terms of pain

relief, improvement of physical function, and quality of life.

To rank the different preparations according to their effectiveness.

To provide a risk-benefit perspective of the different preparations (taking into

consideration results of other network meta-analysis on cardiovascular and

gastrointestinal safety).


September 25, 2012 3 | 9

Criteria for considering studies for this review

Types of studies

Randomized-controlled trials with at least 100 patients per trial arm. Trials only published as

abstract will be excluded.

Types of participants

Patients with osteoarthritis of the hip or knee (trials with mixed populations must either report

results separately or must have included at least 80% patients in the relevant comparisons in

order to be eligible).

Types of interventions

Comparisons of oral

Placebo,

Paracetamol, or

Non-steroidal anti-inflammatory drug(s) included in our safety analyses (GI and CV).

Consequently, the following comparisons are eligible: placebo vs. paracetamol, placebo vs.

NSAID, paracetamol vs. NSAID, NSAID vs. NSAID.

NSAIDs not licensed in the USA, UK, Germany and Switzerland will be excluded.

Types of outcome measures

Primary outcome

The primary outcome measure will be reduction in pain. Several methods to assess pain are

employed in clinical trials in osteoarthritis. We will use a hierarchy of pain outcomes that is

informed by a PhD project at the Institute of Social and Preventive Medicine Bern and extract

the outcome that is highest in this hierarchy (5).

Secondary outcomes

Function-related and quality-of-life outcomes will be extracted as secondary outcomes

whenever available. As with the pain-related primary outcomes we will employ a hierarchy

informed by the PhD project.

Search methods for identification of studies

Electronic searches

The Cochrane Central Register of Controlled Trials (CENTRAL) will be searched from

inception until December 31th, 2010 using the following search terms:

Text words

osteoarthriti* OR

osteoarthro* OR


September 25, 2012 4 | 9

gonarthriti* OR

gonarthro* OR

coxarthriti* OR

coxarthro* OR

arthros* OR

arthrot*.

MeSH terms

Osteoarthritis (explode all trees)

N.B. this is the search strategy used for the PhD (Bruo da Costa).

Searching other ressources

We will screen reference lists of all obtained articles, including relevant reviews. Finally, we

will search clinical trial registries through the WHO International Clinical Trials Registry

Platform (ICTRP; http://apps.who.int/trialsearch/) .

Data collection and analysis

Selection of studies

Two review authors will independently evaluate all identified potentially eligible titles and

abstracts for eligibility. Disagreements will be resolvedby discussion. No language

restrictions will be applied. If multiple reports describe the same trial, we will consider the

most comprehensive information. In case of discrepencies, information submitted to

regulatory authorities will take precedence over peer-reviewed information and non-peer

reviewed information and more up-to-date information will take precedence over older

information.

Data extraction and management

Two review authors will extract trial information independently using a standardised, piloted

data extraction form accompanied by a codebook. Disagreements will be resolved by

consensus or discussion with a third author. The following information will be extracted:

Trial design (including eligibility criteria of patients)

Publication status

Type and source of financial support

Trial size

Details of intervention including dosage and treatment duration

Patient characteristics (average age, gender, mean duration of symptoms)


September 25, 2012 5 | 9

Type of pain- or function-related outcome

Duration of follow up

Outcome data for each timepoint of interest (see below: ). When necessary, we will

approximate means and measures of dispersion from figures in the reports. For cross-

over trials, we will extract data from the first period only, because of possible carry-

over effects. Whenever possible, we will use results from an intention-to-treat analysis.

If we can not calculate effect sizes, we will contact the authors for additional data.

Assessment of risk of bias in included studies

Two review authors will independently assess randomisation, blinding, and adequacy of

analyses. Disagreements will be resolved by consensus. We will assess two components of

randomisation: generation of allocation sequences and concealment of allocation. We will

consider the generation adequate if it resulted in unpredictable allocation sequences;

mechanisms considered adequate include random-number tables, computer-generated random

numbers, minimisation, coin tossing, shuffling of cards, and drawing of lots. We will consider

allocation concealment adequate if the investigators responsible for patient selection were

unable to deduce before allocation which treatment was next; methods considered adequate

included central randomisation and sequentially numbered, sealed, opaque envelopes. We will

consider blinding of the patients adequate if the interventions were explicitly described as

indistinguishable. We will consider analyses adequate if all randomised patients were

included in the analysis according to the intention-to-treat principle.

Measures of treatment effect

All outcomes will be continuous outcomes expressed as effect sizes over time. Outcome data

will be extracted and analysed for the following timepoints of interest: up to 3 months, 6

months, 9 months, 12 months, 15 months, 18 months, 21 months, 24 months, 27 months, 30

months, 33 months, 36 months (all with in a window of 1.5 months; if a trial reports more

than one measurement per time window the nearest to the timepoint of interest will be used

for the analysis).

Effect sizes will be calculated by dividing the differences in mean values in a time-window

between treatment groups by the median pooled standard deviation observed across all time-

points in a trial (6). If standard deviations are not provided, we will calculate them from

standard errors, confidence intervals, or p-values as described elsewhere (5, 7). To allow

intuitive interpretation of pooled effects, we will back transform effect sizes to differences on

a 10 cm visual analogue scale (VAS) on the basis of a median pooled SD of 2.5 cm found in

large-scale osteoarthritis trials that assessed pain on a 10 cm VAS (8). In addition, we will

compare results with the pre-specified minimal clinically important difference which is based

on the median minimal clinically important difference found in recent studies in patients with

osteoarthritis (9-12) of 0.37 standard deviation units, corresponding to 0.9 cm on a 10 cm

VAS.


September 25, 2012 6 | 9

Data synthesis

We will use a Bayesian random effects model which fully preserves randomized treatment

comparisons within trials and which allows for multiple follow-up times (13). It fully

preserves the within-trial randomized treatment comparison of each trial while combining all

available comparisons between treatments, and accounts for multiple comparisons within a

trial in case of more than two treatment arms (14, 15). For the analysis of effect sizes, the

model include random-effects at the level of trials and time-points. It accounts for the

correlation of outcome data reported at different time-points within a trial and allows the

estimation of the between-trial variance of treatment effects τ2. The between-trial

heterogeneity will be estimated from the median between-trial variance τ² observed in the

posterior distribution (accompanied by the 5th and 95th percentile). To determine whether the

variation of treatment effects over time is over and above of what would be expected by

chance, we will calculate a p-value for heterogeneity across time-points of follow-up (16).

The p-value will be derived from the proportion of observations of the posterior distribution

of the variance observed across time-points within trials smaller than or equal to the within-

trial variance typically found in large osteoarthritis trials (0.01 expressed on an effect size

scale, 0.0625 expressed on a 10 cm VAS). We will use the following prior distributions for

the heterogeneity parameters: a gamma distribution for between-trial heterogeneity (1/τ² ~

gamma(0.001,0.001)I(0,2000)), and a uniform distribution for between-timepoint

heterogeneity (τ ~ unif(0,50)). In a sensitivity analysis we will use the uniform prior also for

the between-trial heterogeneity. The consistency of the network will be determined by use of

inconsistency factors: the estimated difference between the effect size from direct

comparisons within randomized trials and the effect size from indirect comparisons between

randomized trials with one intervention in common (17). Estimates of variation and

consistency will be presented based on back transformations to differences on a 10 cm VAS.

Goodness of fit will be assessed by use of Q-Q-plots.

Analyses will be performed using Markov chain Monte Carlo methods. Convergence of

Markov chains will be deemed to be achieved if plots of the Gelman-Rubin statistics indicate

that widths of pooled runs and individual runs stabilize around the same value and their ratio

around one (18). We will use Stata (StataCorp LP 2005. Stata Statistical Software: Release 11.

College Station, TX) and WinBUGS (MRC Biostatistics Unit 2007. Version 1.4. Cambridge,

UK) for all analyses.

Additional analyses

To explore possible time trends of treatment estimates in the trials we will include a linear

term for time as covariate in the analyses. If data allows non-linear terms for time will be also

explored.

To explore robustness of results, we will include characteristics of the trials as covariates in

the network meta-analysis to estimate effects according to concealment of allocation;

intention-to-treat analysis; high methodological quality defined as adequate concealment of

allocation, adequate blinding of patients and the presence of an intention-to-treat analysis;


September 25, 2012 7 | 9

source of funding (industry independent versus other); minimum joint pain required for entry

into the trial. P-values for interaction between trial characteristics and treatment effect will be

derived from the posterior distribution of covariates and can be interpreted in the same way as

a traditional p-value for interaction (19). Finally, we will perform pair-wise meta-analyses

with random-effects at the level of trials and time-points, as well as a simpler network

approach including only one treatment effect per trial (absolute pain intensity at the longest

follow-up available).

Role of the funding source

ARCO foundation will have no role in study design, data collection, data synthesis, data

interpretation, writing the report, or the decision to submit the manuscript for publication.

None of the authors is affiliated with or funded by any manufacturer of any of the agents

evaluated in this study.

References

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rheumatoid arthritis: a systematic review and economic evaluation. Health Technol Assess.

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combining direct and indirect evidence. BMJ. 2005;331:897-900. [PMID: 16223826]

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chondroitin for osteoarthritis of the knee or hip. Ann Intern Med. 2007;146:580-90. [PMID:

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epidemiological study. BMJ. 2009;339:b3244.

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September 25, 2012 9 | 9

Change history

Changes from version December 7th, 2010 to version September 25, 2012

1. Clarification that only NSAIDs included in our safety analyses (CV and GI) are

eligible for this analysis.

supplementary appendix - the lancet€¦ · supplementary appendix ... ketonal or fastum gel or...

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