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A flexible stochastic curtailing procedure for the log-rank test
O M Bautista1, R P Bain, J M Lachin
1The Biostatistics Center, Department of Statistics, The George Washington University, Rockville, MD, USA. omb@biostat.bsc.gwu.edu
Controlled Clinical Trials
|October 6, 2000
Summary
This study introduces a method to evaluate clinical trial futility using conditional power for time-to-event outcomes. It helps determine if trials should stop early for safety or lack of efficacy, aiding data monitoring committees.
Area of Science:
- Clinical Trials
- Biostatistics
- Survival Analysis
Background:
- Data monitoring committees assess clinical trial futility for safety and ethical reasons.
- Conditional power evaluation is crucial for interim trial analysis, especially with adverse events.
- The log-rank test is standard for time-to-event outcomes, but its conditional power is under-explored.
Purpose of the Study:
- To present a method for calculating conditional power for the log-rank test in clinical trials.
- To provide a tool for data monitoring committees to assess trial futility at interim analyses.
- To enhance decision-making regarding trial continuation based on observed data and potential adverse effects.
Main Methods:
- The study describes a novel method for evaluating conditional power.
- This method is applied to the log-rank test used in time-to-event analyses.
- It generalizes a Markov chain approach for unconditional power computation to conditional power.
Main Results:
- A flexible method for conditional power calculation using the log-rank test is detailed.
- The approach accommodates various factors like patient entry, loss to follow-up, and noncompliance.
- This provides a robust framework for assessing trial futility under diverse clinical trial scenarios.
Conclusions:
- The developed method offers a valuable tool for assessing clinical trial futility.
- It supports data monitoring committees in making informed decisions about trial continuation.
- This enhances the safety and efficiency of clinical research by enabling early detection of non-beneficial treatments.
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