CROSS-STUDY HIERARCHICAL MODELING OF STRATIFIED CLINICAL TRIAL DATA

Abstract

Hierarchical random-effects models can be used to estimate treatment or other covariate effects in single-study analyses coordinated over multiple clinical units and can also be extended to a wide variety of cross-study applications. After reviewing the single-study case, we use data from five trial protocols to look for units that tend to have treatment effects consistently above or below the study-specific grand mean across several studies. As a first step, we summarize the patient-level data as study-specific and unit-specific estimated treatment effects and standard errors using independent Cox regression models. We then compare the results of a hierarchical model using these data summaries as input to those produced by a more fully Bayesian method that uses the actual patient-level survival data. We also compare various different models using a deviance information criterion, a recent extension of the Akaike information criterion designed for hierarchical models. Our procedure appears to be effective at answering the question whether certain clinical units of the Terry Beirn Community Programs for Clinical Research on AIDS are better than others at identifying treatment effects where they exist.

Keywords:

• AIDS
• Bayesian methods
• Cross-protocol analysis
• Markov chain Monte Carlo
• Multicenter study
• Random effects models

Acknowledgments

The research of the first author was supported in part by National Institute of Mental Health (NIMH) Grant R01-MH56639 and National Institute of Allergy and Infectious Diseases (NIAID) Contract NO1-AI05073; that of the second author was supported in part by NIAID Grant R01-AI41966; and that of the third author was supported in part by NIAID Contract NO1-AI05073 and National Institute of Dental Research (NIDR) Grant P30-DE09737. Much of this work was carried out while the first author was a graduate student in the Division of Biostatistics at the University of Minnesota. The authors thank Professor Tom Louis for helpful discussions, and the Community Programs for Clinical Research on AIDS, also supported by NIAID Contract NO1-AI05073, for permission to analyze the data presented herein.