Bayesian variable selection for high dimensional predictors and self-reported outcomes.
Background: The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to error. Diagnosis of silent events may also occur through the use of imperfect laboratory-base...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 20; no. 1 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
9/7/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145535902&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145535902 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 9/7/2020 vid: 20 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 145535902 145535902 NLM32894123 10.1186/s12911-020-01223-w NLM32894123 145535902 ppct: 1 formats: tig: atl: Bayesian variable selection for high dimensional predictors and self-reported outcomes. aug: au: Gu, Xiangdong Tadesse, Mahlet G Foulkes, Andrea S Ma, Yunsheng Balasubramanian, Raji affil: Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, USA sug: subj: Diabetes Mellitus, Type 2 Diabetes Mellitus, Type 2 Diagnosis Diabetes Mellitus, Type 2 Epidemiology Prospective Studies Female Self Report Probability Polymorphism, Genetic Female ab: Background: The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to error. Diagnosis of silent events may also occur through the use of imperfect laboratory-based diagnostic tests. In this paper, we describe an approach for variable selection in high dimensional datasets for settings in which the outcome is observed with error.Methods: We adapt the spike and slab Bayesian Variable Selection approach in the context of error-prone, self-reported outcomes. The performance of the proposed approach is studied through simulation studies. An illustrative application is included using data from the Women's Health Initiative SNP Health Association Resource, which includes extensive genotypic (>900,000 SNPs) and phenotypic data on 9,873 African American and Hispanic American women.Results: Simulation studies show improved sensitivity of our proposed method when compared to a naive approach that ignores error in the self-reported outcomes. Application of the proposed method resulted in discovery of several single nucleotide polymorphisms (SNPs) that are associated with risk of type 2 diabetes in a dataset of 9,873 African American and Hispanic participants in the Women's Health Initiative. There was little overlap among the top ranking SNPs associated with type 2 diabetes risk between the racial groups, adding support to previous observations in the literature of disease associated genetic loci that are often not generalizable across race/ethnicity populations. The adapted Bayesian variable selection algorithm is implemented in R. The source code for the simulations are available in the Supplement.Conclusions: Variable selection accuracy is reduced when the outcome is ascertained by error-prone self-reports. For this setting, our proposed algorithm has improved variable selection performance when compared to approaches that neglect to account for the error-prone nature of self-reports. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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