Department of Medicine
Biostatistics | Clinical Epidemiology | Epidemiology
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.
Bayesian variable selection, High dimensional data, Self-reports
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Copyright The Author(s). 2020. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
DOI of Published Version
Gu X, Tadesse MG, Foulkes AS, Ma Y, Balasubramanian R. Bayesian variable selection for high dimensional predictors and self-reported outcomes. BMC Med Inform Decis Mak. 2020 Sep 7;20(1):212. doi: 10.1186/s12911-020-01223-w. PMID: 32894123; PMCID: PMC7487595. Link to article on publisher's site
BMC medical informatics and decision making
Gu X, Tadesse MG, Foulkes AS, Ma Y, Balasubramanian R. (2020). Bayesian variable selection for high dimensional predictors and self-reported outcomes. Open Access Publications by UMMS Authors. https://doi.org/10.1186/s12911-020-01223-w. Retrieved from https://escholarship.umassmed.edu/oapubs/4348
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.