Robust estimates of regional treatment effects in multiregional randomized clinical trials with semiparametric logistic model.

Abstract

In multiregional randomized clinical trials (MRCTs), determining the regional treatment effect of a new treatment over an existing one is important to both the sponsor and related regulatory agencies. Also of particular interest is to test the null hypothesis that the treatment benefit is the same among all the regions. Existing methods are mainly for continuous endpoint and use parametric models, which are not robust. MRCTs are known for facing increased variation and heterogeneity and a robust model for its design and analysis would be desirable. We consider clinical trials with a binary primary endpoint and propose a robust semiparametric logistic model which has a known parametric and an unknown nonparametric component. The parametric component represents our prior knowledge about the model, and the nonparametric part reflects uncertainty. Compared to the classic logistic model for this problem, the proposed model has the following advantages: robust to model assumption, more flexible and accurate to model the relationship between the response and covariates, and possibly more accurate parameter estimates. The model parameters are estimated by profile maximum likelihood approach, and the null hypothesis of regional treatment difference being the same is tested by the profile likelihood ratio statistic. Asymptotic properties of the estimates are derived. Simulation studies are conducted to evaluate the performance of the proposed model, which demonstrated clear advantages over the classic logistic model. The method is then applied to analyzing a real MRCT.