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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

Abstract

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. https://doi.org/10.1214/11-STS381

Information

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

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

Rights: Copyright © 2012 Institute of Mathematical Statistics

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Vol.27 • No. 3 • August 2012
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