Abstract:

Covariate-adaptive randomization is increasingly used in drug clinical trials because it helps balance prognostic factors that may affect trial outcomes between treatment groups. When performing statistical analyses of such trials, researchers usually consider it necessary to adjust for the covariates used in randomization. Therefore, regression models and stratified analyses are commonly employed for statistical inferences. However, no systematic studies have been conducted to discuss the rationality and applicability of these methods. To address this gap, the present study reviews the existing literature, examines the adaptability of traditional statistical inference methods for trials using covariate-adaptive randomization, and introduces the "robust" statistical inference methods proposed in recent years. In addition, to facilitate practical implementation by clinical researchers, example code is provided to demonstrate the application of these methods.

Key words: adaptive-randomization, stratified analysis, covariate-adjusted, robust analysis method, software operation