LAUSR

ABSTRACT For randomized clinical trials, subjects’ variance structures may vary over time among treatment groups, resulting in the heteroscedasticity of residuals in a regression analysis. Commonly used methods that assume equal variance among all treatment groups may not be able to control for a type I error. When the variances are indeed the same across treatment groups, an equal randomization allocation ratio will yield the greatest study power. However, out of ethical concern or urgent need for rare disease clinical trials, more patients may have to be allocated to the study drug arm. In these situations, an unequal randomization ratio should be considered. We propose a group variance–covariance and structures-based method to adapt the randomization ratio after interim analysis. We use simulations to compare commonly used statistical methods for continuous endpoints in assessing the impact of heteroscedasticity in equal and unequal randomization ratios and examine the extent to which the findings are affected by missing data.