A Bayesian subgroup analysis using collections of ANOVA models. Biom J 2017 Jul;59(4):746-766
Date
03/21/2017Pubmed ID
28319254DOI
10.1002/bimj.201600064Scopus ID
2-s2.0-85021725444 (requires institutional sign-in at Scopus site) 5 CitationsAbstract
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 PAuthor
Purushottam W. Laud PhD Professor in the Institute for Health and Equity department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsAnalysis of Variance
Bayes Theorem
Biometry
Humans
Models, Statistical
Probability