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Random intercept hierarchical linear model for multi-regional clinical trials.

Chunkyun Park1, Seung-Ho Kang1

  • 1Department of Statistics and Data Science Department of Applied statistics, Yonsei University, Seoul, Korea.

Journal of Biopharmaceutical Statistics
|January 30, 2023
PubMed
Summary

Hierarchical linear models analyze multi-regional clinical trials by accounting for regional factors. The random intercept model effectively controls type I error rates for overall treatment effects, especially when regional differences are minimal.

Keywords:
between-cluster variabilityextrinsic factorintrinsic factormulti-level modelrandom coefficient model

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Statistical Modeling

Background:

  • Multi-regional clinical trials (MRCTs) require statistical models that account for regional variations.
  • Hierarchical linear models (HLMs) are suitable for MRCTs, acknowledging shared patient factors within regions.

Purpose of the Study:

  • To investigate the statistical properties of HLMs with random intercepts in MRCTs.
  • To compare the random intercept HLM with a random slope HLM regarding their performance and applicability.

Main Methods:

  • Statistical analysis of hierarchical linear models, specifically focusing on random intercept and random slope variations.
  • Simulation studies were conducted to evaluate model performance and derive selection criteria.

Main Results:

  • The random intercept HLM demonstrates an advantage in controlling type I error rates for overall treatment effects when regional differences are negligible.
  • Criteria for selecting between random intercept and random slope HLMs were established based on the magnitude of regional treatment effect variation.

Conclusions:

  • The random intercept HLM is a valuable tool for MRCTs, particularly for maintaining statistical integrity when regional treatment effects are similar.
  • The study provides practical guidance for choosing appropriate HLM structures in MRCTs based on simulated evidence.