Medical College of Wisconsin
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A Bayesian subgroup analysis using collections of ANOVA models. Biom J 2017 Jul;59(4):746-766 PMID: 28319254

Pubmed ID

28319254

DOI

10.1002/bimj.201600064

Abstract

We develop a Bayesian approach to subgroup analysis using ANOVA models with multiple covariates, extending an earlier work. We assume a two-arm clinical trial with normally distributed response variable. We also assume that the covariates for subgroup finding are categorical and are a priori specified, and parsimonious easy-to-interpret subgroups are preferable. We represent the subgroups of interest by a collection of models and use a model selection approach to finding subgroups with heterogeneous effects. We develop suitable priors for the model space and use an objective Bayesian approach that yields multiplicity adjusted posterior probabilities for the models. We use a structured algorithm based on the posterior probabilities of the models to determine which subgroup effects to report. Frequentist operating characteristics of the approach are evaluated using simulation. While our approach is applicable in more general cases, we mainly focus on the 2 × 2 case of two covariates each at two levels for ease of presentation. The approach is illustrated using a real data example.

Author List

Liu J, Sivaganesan S, Laud PW, Müller P

Author

Purushottam W. Laud PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin




Scopus

2-s2.0-85021725444

MESH terms used to index this publication - Major topics in bold

Algorithms
Analysis of Variance
Bayes Theorem
Biometry
Humans
Models, Statistical
Probability
jenkins-FCD Prod-331 a335b1a6d1e9c32173c9534e6f6ff51494143916