Multivariate Bayesian logistic regression for analysis of clinical study safety issues. (English) Zbl 1331.62416

DuMouchel, William

Stat. Sci. 27, No. 3, 319-339 (2012).

Summary: 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.

MSC:

62P10	Applications of statistics to biology and medical sciences; meta analysis
62F15	Bayesian inference
62-07	Data analysis (statistics) (MSC2010)
62J02	General nonlinear regression
62J07	Ridge regression; shrinkage estimators (Lasso)

Keywords:

adverse drug reactions; Bayesian shrinkage; drug safety; data granularity; hierarchical Bayesian model; parallel logistic regressions; sparse data; variance component estimation

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