Hierarchical Generalized Linear Models for Multiregional Clinical Trials

Junhui Park, Seung Ho Kang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)358-367
Number of pages10
JournalStatistics in Biopharmaceutical Research
Issue number3
Publication statusPublished - 2022

Bibliographical note

Funding Information:
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).

Publisher Copyright:
© 2021 American Statistical Association.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Pharmaceutical Science


Dive into the research topics of 'Hierarchical Generalized Linear Models for Multiregional Clinical Trials'. Together they form a unique fingerprint.

Cite this