Hierarchical Linear Models for Multiregional Clinical Trials

Abstract

Data observed in multiregional clinical trials are structurally hierarchical in the sense that the patient population consists of several regions and patients are nested within their own regions. To reflect such hierarchical structure, in this article, we propose two-level hierarchical linear models in which the level-1 model is based on patient-level data such as treatment indicator and age, and the level-2 model is based on region-level data such as medical practices. The fixed effect model and the continuous random effect model are shown to be special cases of hierarchical linear models. We conducted simulation studies to investigate the empirical Type I error rates of three methods for testing the overall treatment effect. The performance of the testing method with sample ratios as weights and the empirical Bayes estimator for between-region variability is better than that of the other two testing methods.

KEYWORDS:

• Between-cluster variability
• Mixed effect model
• Permutation test
• 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 (2016R1D1A1A09916819).