Journal
STATISTICS IN BIOPHARMACEUTICAL RESEARCH
Volume 14, Issue 3, Pages 358-367Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/19466315.2020.1862702
Keywords
Bernoulli distribution; Between-cluster variability; Exponential family; Generalized linear mixed model; Random coefficient model
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2020R1F1A1A0 1048240]
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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.
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.
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