Hierarchical Generalized Linear Models for Multiregional Clinical Trials

Abstract

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.

KEYWORDS:

• Bernoulli distribution
• Between-cluster variability
• Exponential family
• Generalized linear mixed model
• Random coefficient model

Additional information

Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2020R1F1A1A0 1048240).