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Bayesian Variable Selection in Multilevel Item Response Theory Models with Application in Genomics
Author(s) -
Fragoso Tiago M.,
de Andrade Mariza,
Pereira Alexandre C.,
Rosa Guilherme J. M.,
Soler Júlia M. P.
Publication year - 2016
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21960
Subject(s) - computer science , markov chain monte carlo , covariate , feature selection , selection (genetic algorithm) , variable (mathematics) , markov chain , machine learning , bayesian probability , model selection , data mining , econometrics , artificial intelligence , mathematics , mathematical analysis
The goal of this paper is to present an implementation of stochastic search variable selection (SSVS) to multilevel model from item response theory (IRT). As experimental settings get more complex and models are required to integrate multiple (and sometimes massive) sources of information, a model that can jointly summarize and select the most relevant characteristics can provide better interpretation and a deeper insight into the problem. A multilevel IRT model recently proposed in the literature for modeling multifactorial diseases is extended to perform variable selection in the presence of thousands of covariates using SSVS. We derive conditional distributions required for such a task as well as an acceptance‐rejection step that allows for the SSVS in high dimensional settings using a Markov Chain Monte Carlo algorithm. We validate the variable selection procedure through simulation studies, and illustrate its application on a study with genetic markers associated with the metabolic syndrome.