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A hierarchical rank test for crossover trials with censored data
Erica Brittain1, Dean Follmann
1Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, USA. ebrittain@niaid.nih.gov
Statistics in Medicine
|December 6, 2011
Summary
This study introduces a hierarchical ranking method for analyzing survival time in crossover clinical trials. The approach prioritizes event occurrence and then event timing, offering greater statistical power in specific scenarios.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Survival Analysis
Background:
- Analyzing survival time in crossover clinical trials presents unique challenges.
- Existing methods may not fully capture the dual goals of preventing and delaying clinical events.
Purpose of the Study:
- To propose a novel hierarchical ranking approach for survival time analysis in crossover clinical trials.
- To compare the proposed method with existing procedures, such as the Feingold and Gillespie method.
- To evaluate the clinical relevance and statistical power of the hierarchical ranking method.
Main Methods:
- A two-stage ranking system: primary ranking for event occurrence, secondary ranking for event time.
- Comparison of the hierarchical method against established survival analysis techniques.
- Application to scenarios including specific censoring patterns, cure models, and continuous outcomes.
Main Results:
- The hierarchical ranking method demonstrates comparable power to existing methods in many settings.
- Substantially greater statistical power is observed under certain censoring patterns and cure models.
- The method shows potential for enhanced clinical relevance in therapeutic evaluations.
Conclusions:
- The proposed hierarchical ranking method offers a powerful and clinically relevant approach to survival time analysis in crossover trials.
- This method is particularly advantageous in scenarios with specific censoring or when treatment induces significant delays or cures.
- The approach is versatile and applicable to various outcome types, including continuous data censored by detection limits.
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Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
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