Sample Size Assessment and Interim Analysis Strategies for Survival Trial Designs With Covariate‐Adaptive Randomization

• Published in: Statistics in medicine Vol. 44; no. 13-14; pp. e70149 - n/a
• Main Authors: Su, Pei‐Fang, Wu, Chieh‐Chi
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2025; Wiley Subscription Services, Inc
• Subjects: biased coin randomization; Computer Simulation; cox model; Data Interpretation, Statistical; Humans; Hypothesis testing; Lung Neoplasms - drug therapy; Lung Neoplasms - mortality; Models, Statistical; power; Random Allocation; Randomized Controlled Trials as Topic - methods; Randomized Controlled Trials as Topic - statistics & numerical data; Research Design; Sample Size; sensitivity analysis; stratified permuted block randomization; Survival Analysis; type I error rate; cox model; type I error rate; power; stratified permuted block randomization; biased coin randomization; sensitivity analysis
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.70149

ABSTRACT Estimating the required sample size is an essential task for a clinical trial. It ensures statistical power and makes it possible to draw meaningful conclusions from the study results. Moreover, to minimize treatment imbalances within each covariate subgroup, covariate‐adaptive randomization is a popular method. The first aim of this study is to investigate the required sample size for covariate‐adaptive randomization based on survival outcomes. We evaluate the testing performance using the calculated sample size under simple randomization, stratified permuted block randomization, and covariate‐adaptive biased coin randomization. To provide preliminary insights into the trial's progress and potential efficacy, an interim analysis is commonly conducted. The second aim of the study is to provide a strategy for interim analysis in covariate‐adaptive randomization trials, which involves stopping the trial early based on accumulating data if one treatment arm proves to be significantly more effective or harmful than the others. Because the data collected before and after the interim monitoring time are dependent, performing multiple tests may inflate the overall Type I error rate. We thus re‐estimate the required sample size, propose a valid hypothesis testing method for interim analysis, and study the underlying theoretical properties of the testing statistics, incorporating covariate‐adaptive randomization. The performance of the proposed formula and interim strategy is evaluated through comprehensive simulations, including a sensitivity analysis. We also provide the R code to benefit the readers. Finally, two survival trials, the RE01 trial and a lung cancer study, are treated as pilot studies, and we show that the proposed strategies are valid under covariate‐adaptive randomization.