Hierarchical Proportional Hazards Regression Models for Highly Stratified Data

Authors

  • Bradley P. Carlin,

    Corresponding author
    1. Division of Biostatistics, School of Public Health, University of Minnesota, Box 303 Mayo Building, Minneapolis, Minnesota 55455, U.S.A.
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  • James S. Hodges

    1. Division of Biostatistics, School of Public Health, University of Minnesota, Box 303 Mayo Building, Minneapolis, Minnesota 55455, U.S.A.
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*email:brad@muskie.biostat.umn.edu

Abstract

Summary. In clinical trials conducted over several data collection centers, the most common statistically defensible analytic method, a stratified Cox model analysis, suffers from two important defects. First, identification of units that are outlying with respect to the baseline hazard is awkward since this hazard is implicit (rather than explicit) in the Cox partial likelihood. Second (and more seriously), identification of modest treatment effects is often difficult since the model fails to acknowledge any similarity across the strata. We consider a number of hierarchical modeling approaches that preserve the integrity of the stratified design while offering a middle ground between traditional stratified and unstratified analyses. We investigate both fully parametric (Weibull) and semiparametric models, the latter based not on the Cox model but on an extension of an idea by Gelfand and Mallick (1995, Biometrics51, 843–852), which models the integrated baseline hazard as a mixture of monotone functions. We illustrate the methods using data from a recent multicenter AIDS clinical trial, comparing their ease of use, interpretation, and degree of robustness with respect to estimates of both the unit-specific baseline hazards and the treatment effect.

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