Hierarchical Generalized Linear Models for Multiregional Clinical Trials

Multiregional clinical trials have a hierarchical data structure because several regions form a patient population and individual patients are nested within their own regions. Data are obtained from two different levels: regions and patients. To incorporate such a hierarchical structure, hierarchical linear models were proposed for the response variables following a normal distribution by Kim and Kang. In this article, we extend the hierarchical linear models to propose hierarchical generalized linear models (HGLMs) so that the response variables can follow the exponential family. We describe the details of the model when the response variable follows the Bernoulli distribution and the Poisson distribution. Simulation studies show that the empirical powers of the HGLM are greater than random effects model when region-level covariates are incorporated.

Automated summary

The article explores the application of hierarchical linear models and hierarchical generalized linear models in multiregional clinical trials, discussing the details of the models under different response variable distributions. Simulation studies indicate that the empirical powers of HGLM exceed those of random effects models when incorporating region-level covariates.