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Assessment of Responder Analyses for Patient-Reported Outcomes With Varying Variability Between Intervention Groups:
Joseph C Cappelleri1, Jia Ma1, Manuela Di Fusco2
1Statistical Research and Data Science Center, Pfizer Inc, Groton, Connecticut, USA.
Pharmaceutical Statistics
|February 26, 2026
Summary
Responder analyses dichotomize continuous outcomes, but this study explores varying standard deviations to find optimal thresholds. Responsible use of responder analyses can aid in interpreting treatment benefits.
Area of Science:
- Biostatistics
- Clinical Trial Methodology
- Patient-Reported Outcomes
Background:
- Continuous patient outcomes are often dichotomized into responders and non-responders.
- Responder analyses simplify interpretation but face scrutiny due to potential biases.
- Previous research focused on normal distributions with common standard deviations.
Purpose of the Study:
- To quantify and depict responder analyses in practical situations with varying group standard deviations.
- To identify thresholds that maximize the responder effect.
- To provide advanced understanding for prudent interpretation of responder analyses.
Main Methods:
- Expanded upon previous research by considering varying standard deviations.
- Utilized conceptual, theoretical, and illustrative underpinnings.
- Quantified and depicted responder analyses for broader applicability.
Main Results:
- Identified thresholds yielding the maximum responder effect.
- Determined the maximum possible responder effect under varying standard deviations.
- Provided new quantitative learnings and visual insights.
Conclusions:
- Responder analyses have limitations and require cautious application.
- When used responsibly, responder analyses can augment interpretation of patient-reported outcomes.
- Responder analyses, alongside cumulative distribution functions, can aid in assessing overall clinical benefit.
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