Evaluation of performance of some enrichment designs dealing with high placebo response in psychiatric clinical trials.

Dealing with high placebo response remains a big challenge to conventional clinical trials for psychiatric disorders. A widely-used design strategy is to implement a placebo lead-in phase prior to randomization. The sequentially parallel design (SPD) proposed by Fava et al., which contains two consecutive double-blind treatment stages, has recently been promoted to reduce both the high placebo response and the required sample size in clinical trials for psychiatric disorders. Our work aims to study these two design strategies and evaluate the relevant statistical approaches for continuous measures under SPD in the presence of missing data. Based on the FDA archived database, we found that a longer placebo lead-in period seemed to help in identifying more placebo responders and thus increase the chance to detect a drug-placebo difference on continuous efficacy endpoint. Using a simple weighted ordinary least square test statistic Z(OLS), we analytically showed that, under the SPD with re-randomization of placebo non-responders at the second stage (SPD-ReR), Z(OLS) can be used as a viable alternative to the weighted test statistic based on seemingly unrelated regression estimate Z(SUR) proposed by Tamura and Huang to assess treatment efficacy. Results from simulation study comparing three imputation methods (last-observation-carried-forward approach, multiple imputation, and mixed-effects model for repeated measures (MMRM)) demonstrate that, when data are missing-at-random under SPD-ReR and the dropout rate is moderate, the weighted test statistic based on MMRM estimates appears to be the most robust test statistic for SPD-ReR in terms of type I error control, power performance, and estimation accuracy.