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Random regression models for multicenter clinical trials data.

D Hedeker1, R D Gibbons, J M Davis

  • 1University of Illinois, Chicago.

Psychopharmacology Bulletin
|January 1, 1991
PubMed
Summary
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This study introduces a random regression model (RRM) for multicenter clinical trials. The RRM effectively analyzes data with varying subjects per center and assesses subject and center-level influences on outcomes.

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Psychiatric Research

Background:

  • Multicenter clinical trials generate complex data with inherent variability.
  • Analyzing data from multiple centers requires statistical models that account for hierarchical structures.

Purpose of the Study:

  • To propose and illustrate a random-effects regression model (RRM) for analyzing data from multicenter clinical trials.
  • To highlight the advantages of RRM in handling varying subject numbers and assessing multi-level predictors.

Main Methods:

  • Development of a random-effects regression model (RRM).
  • Application of RRM to analyze data from the National Institute of Mental Health schizophrenia collaborative study.
  • Statistical modeling to control for and estimate intracenter variation.

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Main Results:

  • The RRM accommodates varying numbers of subjects across different centers.
  • The model can assess the influence of both subject-level and center-level variables on clinical outcomes.
  • RRM effectively controls for and quantifies intracenter variation.

Conclusions:

  • The random-effects regression model is a valuable statistical tool for multicenter clinical trials.
  • RRM provides a robust framework for analyzing complex psychiatric data and understanding sources of variation.
  • The model has potential for broader applications in psychiatric research.