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Global rank tests for multiple, possibly censored, outcomes.
Ritesh Ramchandani1, David A Schoenfeld2,3, Dianne M Finkelstein2,3
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave Boston, Massachusetts 02115, U.S.A.. ritesh@mail.harvard.edu.
Biometrics
|January 27, 2016
Summary
This study introduces a new nonparametric global test for analyzing multiple outcomes in clinical trials, particularly for diseases like Amyotrophic Lateral Sclerosis (ALS). The method efficiently assesses treatment effects across various dimensions, including survival, using adaptive weighting for optimal results.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Neurology
Background:
- Clinical trials frequently measure multiple patient outcomes to evaluate treatment efficacy.
- Assessing treatments using multiple endpoints presents significant statistical challenges in test selection and interpretation.
Purpose of the Study:
- To propose a general, flexible, and robust approach for selecting and executing global tests on multiple outcomes in clinical trials.
- To develop a nonparametric global test suitable for censored data and adaptable to unknown outcome weightings.
Main Methods:
- A novel global test based on pairwise subject scoring across multiple endpoints.
- Reduction of pairwise scores to a summary statistic, followed by a rank-sum test.
- Exploration of optimal and adaptive weighting schemes for diverse outcomes.
Main Results:
- The proposed global test requires minimal parametric assumptions and handles censored data effectively.
- The method allows for flexible weighting of different outcomes based on importance and power.
- Simulations demonstrated the performance of the adaptive weighting scheme.
Conclusions:
- The developed global test provides a powerful and adaptable tool for analyzing complex clinical trial data with multiple outcomes.
- This approach is particularly valuable for diseases like ALS, where multiple dimensions of patient health and survival are critical.
- The adaptive weighting strategy addresses practical challenges when optimal weights are unknown.
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