Covariate-adaptive randomization with dropouts

While clinical trials are often regarded as the gold standard by medical researchers and regulatory agencies, the statistical properties of typical randomized controlled trials heavily depend on large sample sizes, which may not always be achievable due to budget constraints or other practical limitations. To address this issue, adaptive designs have gained increasing attention since the 1970s. These designs incorporate modifications in trial procedures or statistical methodologies, aiming to fulfill study objectives more efficiently using smaller sample sizes and shorter durations compared to traditional clinical trials. Specifically, covariate-adaptive designs are developed to reduce the risk of significant covariate imbalances among treatment groups, particularly in studies with limited sample sizes. Additionally, patient dropout during trials significantly impacts statistical inference regarding treatment effects and diminishes statistical power. Ignoring dropout events can result in undesirable participant allocation and substantial treatment imbalances. In this article, we propose novel strategies that explicitly incorporate dropout information into covariate-adaptive randomization. We thoroughly investigate the statistical properties of covariate-adaptive designs adjusted for dropouts. Extensive simulation studies highlight the advantages of the proposed approaches. Our research lays a robust foundation for future developments in covariate-adaptive randomization, specifically addressing dropout challenges.