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Statistical issues in patient reported outcome measures
Jonas Ranstam PhD
RC Syd and Lund University, Lund, Sweden, Email: [email protected]
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Science and uncertainty
“If you thought that science was certain - well, that is just an error on your part.”
Richard P. Feynman
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Uncertainty in clinical research
Generalization from sample to population
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Observedoutcome
Concluded outcome
Inference (naive)
Real outcome
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Observedoutcome
Concluded outcome
Effectmodification
Case-mix
InteractionMis-
classification
Selection
Measurementerrors
Biologicalvariation Co-
morbidity
Real outcome
Inference (scientific)
Uncertaintyacknowledged
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The statistician's task
To eliminate as much uncertainty as possible (by design) and to quantify (in the analysis) what is left.
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Statistical characteristics of PROs
1. Measured on an ordinal scale
2. Discrete distribution
3. Truncated distribution
4. Skewed distribution
5. Floor and/or ceiling effects
Are standard analysis methods appropriate?
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Lillgraven S, Kristiansen IS, Kvien TK. Comparison of utility measures and their relationship with other health status measures in 1041 patients with rheumatoid arthritis. Ann Rheum Dis 2010;69:1762-1767.
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The EQ-5D index
Simulation studies at RC Syd show that
Type-1 error rate (risk of false positive findings)
EQ-5D is best analyzed using methods that assume a Gaussian distribution, at least if n is large, between 20-50. Non-parametric alternatives perform poorly with any n value.
Type-2 error rate (risk of false negative findings)
No single method can be recommended. All investigated methods perform poorly for any distributional shape.
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Complications
Longitudinal analyses
(of change, gain, delta-value, etc.)
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Simulated data (n = 1000)
pre - post
correlation (pre, post) = 0
delta = post - pre
correlation (pre, post - pre) = -0.7
Baseline versus change
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Baseline versus change
50% of the “change” can be explained by baseline.
When comparing “change” in different groups, always adjust for imbalance at baseline (e.g. using ANCOVA).
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RTM - Regression to the mean
If the first measurement of a variable is extreme, the second measurement will tend to be closer to the average.
Note, this is a purely statistical phenomenon.
Galton F. Regression towards mediocrity in hereditary stature. J Anth Inst Gr Br Ire.1886;15:246–263.
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Hypothetical example: SF-36 PF
Baseline: mean = 80, SD = 17Follow up: mean = 80, SD = 17p ≈ 1.0
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Hypothetical example: SF-36 PF
Baseline: mean = 48.7, SD = 8.6Follow up: mean = 59.2, SD = 16.7p < 0.001
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RTM - Regression to the mean
The phenomenon explains the placebo effect in clinical trials and apparent treatment effects found in some studies on homeopathic drugs, bible reading, etc.
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Barnett AG, van der Pols JC, Dobson AJ. Regression to the mean: what it is and how to deal with it. Int J Epidemiol 2005;34:215–220
RTM - Easy to quantify (for Normally distributed endpoints)
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Hypothetical example of RTM in SF-36 PF
Mean = 80, SD = 17, cut off = 60
r RTM0.0 28.4 0.1 25.50.2 22.70.3 19.90.4 17.00.5 14.20.6 11.30.7 8.50.8 5.70.9 2.81.0 0
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Regression-to-the-mean
Evaluation of a single group’s development over time should be avoided, or at least include a comparison with the expected RTM effect.
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Practical consequences
When evaluating change, use a control group.
Adjust for baseline imbalance.
The validity of this procedure with multi-modal data (e.g. the EQ-5D index) is unknown.
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Thank you for your attention!
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Can a multi-scale PRO be used as a primary endpoint in a randomized trial?
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Can a multi-scale PRO be used as a primary endpoint in a randomized trial?
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Can a PRO be used as a primary endpoint in a randomized trial?
PRO as primary endpoint
“A PRO measurement can be the clinical trial’s primary endpoint measure, a co-primary endpoint measure ... or a secondary endpoint measure whose analysis is considered according to a hierarchical sequence.“
FDA. Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims. Guidance for Industry.
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Can a multi-scale PRO be used as a primary endpoint in a randomized trial?
PRO and HRQL
“The term PRO is proposed as an umbrella term to cover both single dimension and multi-dimension measures of symptoms, health-related quality of life (HRQL), health status, adherence to treatment, satisfaction with treatment, etc.”
“In the context of drug approval, HRQL is considered to represent a specific type/subset of PROs, distinguished by its multi-dimensionality.”
EMEA. Reflection paper on the regulatory guidance for the use of health-related quality of life (HRQL) measures in the evaluation of medicinal products.
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Can a multi-scale PRO be used as a primary endpoint in a randomized trial?
HRQL as primary endpoint
“In general, the methodology for assessing the effect on HRQL is similar to the methodology used in any efficacy trial, except for issues related to the nature of the instruments, which are generally composed of multi-items, and multi-domains.
Briefly, it is recommended that HRQL instrument be previously validated for the condition studied...”
EMEA. Reflection paper on the regulatory guidance for the use of health-related quality of life (HRQL) measures in the evaluation of medicinal products.
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FDA. Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims. Guidance for Industry.