Bayesian Variable Selection in Multilevel Item Response Theory Models with Application in Genomics

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art_FRAGOSO_Bayesian_Variable_Selection_in_Multilevel_Item_Response_Theory_2016.PDF (793.45 KB)
Citações na Scopus
2
Tipo de produção
article
Data de publicação
2016
Editora
WILEY-BLACKWELL
DOI
Indexadores
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Autores
FRAGOSO, Tiago M.
ANDRADE, Mariza de
ROSA, Guilherme J. M.
SOLER, Julia M. P.
Autor de Grupo de pesquisa
Editores
Coordenadores
Organizadores
Citação
GENETIC EPIDEMIOLOGY, v.40, n.3, p.253-263, 2016
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
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.
Palavras-chave
stochastic search variable selection, MCMC, data augmentation, metabolic syndrome
URI
https://observatorio.fm.usp.br/handle/OPI/14516
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Coleções
Artigos e Materiais de Revistas Científicas - HC/InCor
Artigos e Materiais de Revistas Científicas - LIM/13