Comprehensive simulation-based comparison of power calculation methods for the log-rank test
Abstract Number:
2994
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Emily Goren (1), Michael LeBlanc (2), John Crowley (1)
Institutions:
(1) Cancer Research and Biostatistics, Seattle, WA, (2) Fred Hutchinson Cancer Research Center, Seattle, WA
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
The log-rank test is widely used in 2-arm clinical trials to compare survival distributions between groups. Design of clinical trials utilizing the log-rank test requires power calculations under the alternative hypothesis. Various distributional approximations for the log-rank test statistic under the alternative hypothesis have been proposed for power determination. Bernstein and Lagakos (1978) use an exponential MLE test for power calculation. Schoenfeld's (1981) method depends on a normal approximation with variance derived under local alternatives. Luo et al. (2019) and Yung and Liu (2020) derive a different asymptotic variance which also requires local alternatives. In practice, a local alternative assumption may unreasonable for large effects and modest sample sizes. Practical guidance is needed to guide selection of a power calculation method. We conduct a comprehensive simulation study comparing power calculation methods for the log-rank test and compare the underlying distributional assumptions of the mean and variance. This work provides guidance for practitioners and highlights the need for deriving the distribution of the log-rank test under general alternatives.
Keywords:
log-rank test|asymptotic theory|clinical trial design|survival analysis| |
Sponsors:
Biometrics Section
Tracks:
Survival Analysis
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