An analytic framework for exploring sampling and observation process biases in genome and phenome-wide association studies using electronic health records.
Large-scale association analyses based on observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, challenges due to nonprobability sampling and phenotype misclassification associated with the use of these da...
| Publicado en: | Statistics in Medicine Vol. 39; no. 14; pp. 1965 - 1980 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
6/30/2020
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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=143547213&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143547213 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 6/30/2020 vid: 39 iid: 14 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 143547213 143547213 145777977 NLM32198773 143547213 10.1002/sim.8524 NLM32198773 143547213 ppf: 1965 ppct: 15 formats: tig: atl: An analytic framework for exploring sampling and observation process biases in genome and phenome-wide association studies using electronic health records. aug: au: Beesley, Lauren J. Fritsche, Lars G. Mukherjee, Bhramar affil: Department of Biostatistics, University of Michigan, Ann Arbor Michigan, USA sug: subj: Sequence Analysis Phenotype Polymorphism, Genetic Michigan Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: Large-scale association analyses based on observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, challenges due to nonprobability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. The extent of the bias introduced by ignoring these factors is not well-characterized. In this paper, we develop an analytic framework for characterizing the bias expected in disease-gene association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis approach, this framework can be used to obtain plausible values for parameters of interest given summary results from standard analysis. We develop an online tool for performing this sensitivity analysis. Simulations demonstrate promising properties of the proposed method. We apply our approach to study bias in disease-gene association studies using electronic health record data from the Michigan Genomics Initiative, a longitudinal biorepository effort within The University Michigan health system. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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