Repeated Measures Analysis of the Sequential Parallel Comparison Design With Normal Responses

Abstract

For the sequential parallel comparison design with normally distributed outcomes and rerandomization at the start of phase 2, we propose fitting separate repeated measures models for the two phases. We show analytically the asymptotic independence between the treatment effect estimators in the two phases. We recommend prespecification of the weights for combining the treatment effects in the two phases and use of the t reference distribution with the Welch–Satterthwaite degrees of freedom for testing the overall treatment effect when the sample size is small. We use simulation studies to demonstrate that the proposed method controls Type I error and has proper coverage of 95% confidence intervals.