Abstract

BackgroundThe 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.MethodsWe 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.ResultsSimulation 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.ConclusionsVariable 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.

Highlights

  • The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts

  • We apply the proposed methods to discover single nucleotide polymorphisms (SNPs) associated with type 2 diabetes risk in the Women’s Health Initiative (WHI) Clinical Trial and Observational Study Single Nucleotide Polymorphism (SNP) Health Association Resource (SHARe), which includes extensive genotypic (>900,000 SNPs) and phenotypic data on 9,873 African American and Hispanic American women.The proposed methods apply when a silent outcome is ascertained through laboratory-based diagnostic procedures that are subject to misclassification

  • We consider a high dimensional dataset in which each feature is a random variable with three levels, reflecting the two possible homozygous (AA, aa) and the single heterozygous (Aa) combination of alleles of a SNP

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Summary

Introduction

The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. A formal likelihood framework was proposed to accommodate sequentially administered, error-prone self-reports or laboratory based diagnostic tests for modeling the association of a targeted set of covariates with the timeto-event outcome of interest [12]. While a rich literature exists to handle estimation and hypothesis testing in the presence of error-prone survival outcomes, none of these approaches can be applied directly to variable selection in high-dimensional data, in which the number of features (p) far exceeds the number of subjects (n). In this setting, standard likelihood based estimation approaches are intractable

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