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Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification
dc.contributor.authorBeesley, Lauren J.
dc.contributor.authorMukherjee, Bhramar
dc.date.accessioned2022-12-05T16:39:15Z
dc.date.available2024-01-05 11:39:13en
dc.date.available2022-12-05T16:39:15Z
dc.date.issued2022-12-10
dc.identifier.citationBeesley, Lauren J.; Mukherjee, Bhramar (2022). "Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification." Statistics in Medicine 41(28): 5501-5516.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/175191
dc.publisherJohn Wiley & Sons, Inc.
dc.subject.otherSEER
dc.subject.otherMichigan Genomics Initiative
dc.subject.othernon-probability sampling
dc.subject.otherpoststratification
dc.subject.otherinverse probability weighting
dc.subject.otherNHANES
dc.subject.otherelectronic health records
dc.titleCase studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelSocial Sciences
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/175191/1/sim9579_am.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/175191/2/sim9579.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/175191/3/sim9579-sup-0001-supinfo.pdf
dc.identifier.doi10.1002/sim.9579
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferenceBeesley LJ, Mukherjee B. Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification. Biometrics. 2022; 78 ( 1 ): 214 - 226.
dc.identifier.citedreferenceNeuhaus JM, Jewell NP. A geometric approach to assess bias due to omitted covariates in generalized linear models author. Biometrika. 1993; 80 ( 4 ): 807 - 815.
dc.identifier.citedreferenceFritsche LG, Igl W, Cooke Bailey JN, et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat Genet. 2016; 48 ( 2 ): 134 - 143. doi: 10.1038/ng.3448.A
dc.identifier.citedreferenceTipton E. How generalizable is your experiment? An index for comparing experimental samples and populations. J Edu Behav Stat. 2014; 39 ( 6 ): 478 - 501.
dc.identifier.citedreferenceDegtiar I, Rose SA. Review of generalizability and transportability arXiv preprint, arXiv:2102.11904.
dc.identifier.citedreferenceDahabreh IJ, Hern�n MA. Extending inferences from a randomized trial to a target population. Eur J Epidemiol. 2019; 34 ( 8 ): 719 - 722. doi: 10.1007/s10654-019-00533-2
dc.identifier.citedreferenceBeesley LJ, Salvatore M, Fritsche LG, et al. The emerging landscape of epidemiological research based on biobanks linked to electronic health records. Stat Med. 2019; 39 ( 6 ): 773 - 800.
dc.identifier.citedreferenceFritsche LG, Gruber SB, Wu Z, et al. Association of Polygenic Risk Scores for multiple cancers in a Phenome-wide study: results from the Michigan genomics initiative. Am J Hum Genet. 2018; 102 ( 6 ): 1 - 14.
dc.identifier.citedreferenceHaneuse S, Daniels M. A general framework for considering selection bias in EHR-based studies: what data are observed and why? eGEMs. 2016; 4 ( 1 ): 1 - 17.
dc.identifier.citedreferenceHuang J, Duan R, Hubbard RA, et al. PIE: a prior knowledge guided integrated likelihood estimation method for bias reduction in association studies using electronic health records data. J Am Med Inform Assoc. 2018; 25 ( 3 ): 345 - 352.
dc.identifier.citedreferenceSinnott JA, Dai W, Liao KP, et al. Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records. Hum Genet. 2014; 133 ( 11 ): 1369 - 1382.
dc.identifier.citedreferenceDuffy SW, Warwick J, Williams AR, et al. A simple model for potential use with a misclassified binary outcome in epidemiology. J Epidemiol Community Health. 2004; 58 ( 8 ): 712 - 717.
dc.identifier.citedreferenceElliot MR. Combining data from probability and non- probability samples using pseudo-weights. Surv Pract. 2009; 2 ( 3 ): 1 - 7.
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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