Open Access
August 2012 Multivariate Bayesian Logistic Regression for Analysis of Clinical Study Safety Issues
William DuMouchel
Statist. Sci. 27(3): 319-339 (August 2012). DOI: 10.1214/11-STS381


This paper describes a method for a model-based analysis of clinical safety data called multivariate Bayesian logistic regression (MBLR). Parallel logistic regression models are fit to a set of medically related issues, or response variables, and MBLR allows information from the different issues to “borrow strength” from each other. The method is especially suited to sparse response data, as often occurs when fine-grained adverse events are collected from subjects in studies sized more for efficacy than for safety investigations. A combined analysis of data from multiple studies can be performed and the method enables a search for vulnerable subgroups based on the covariates in the regression model. An example involving 10 medically related issues from a pool of 8 studies is presented, as well as simulations showing distributional properties of the method.


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William DuMouchel. "Multivariate Bayesian Logistic Regression for Analysis of Clinical Study Safety Issues." Statist. Sci. 27 (3) 319 - 339, August 2012.


Published: August 2012
First available in Project Euclid: 5 September 2012

zbMATH: 1331.62416
MathSciNet: MR3012426
Digital Object Identifier: 10.1214/11-STS381

Keywords: Adverse drug reactions , Bayesian shrinkage , data granularity , drug safety , hierarchical Bayesian model , parallel logistic regressions , sparse data , variance component estimation

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.27 • No. 3 • August 2012
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