Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81924
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dc.contributorDepartment of Applied Mathematics-
dc.creatorWong, KY-
dc.creatorZeng, D-
dc.creatorLin, DY-
dc.date.accessioned2020-04-23T01:56:56Z-
dc.date.available2020-04-23T01:56:56Z-
dc.identifier.issn0162-1459-
dc.identifier.urihttp://hdl.handle.net/10397/81924-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.rights© 2018 American Statistical Associationen_US
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 26 Feb 2019, available online: http://www.tandfonline.com/10.1080/01621459.2018.1514304.en_US
dc.subjectAssociation testsen_US
dc.subjectImputationen_US
dc.subjectIntegrative analysisen_US
dc.subjectMultiple genomics platformsen_US
dc.subjectSemiparametric modelsen_US
dc.subjectSieve estimationen_US
dc.titleRobust score tests with missing data in genomics studiesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1778-
dc.identifier.epage1786-
dc.identifier.volume114-
dc.identifier.issue528-
dc.identifier.doi10.1080/01621459.2018.1514304-
dcterms.abstractAnalysis of genomic data is often complicated by the presence of missing values, which may arise due to cost or other reasons. The prevailing approach of single imputation is generally invalid if the imputation model is misspecified. In this article, we propose a robust score statistic based on imputed data for testing the association between a phenotype and a genomic variable with (partially) missing values. We fit a semiparametric regression model for the genomic variable against an arbitrary function of the linear predictor in the phenotype model and impute each missing value by its estimated posterior expectation. We show that the score statistic with such imputed values is asymptotically unbiased under general missing-data mechanisms, even when the imputation model is misspecified. We develop a spline-based method to estimate the semiparametric imputation model and derive the asymptotic distribution of the corresponding score statistic with a consistent variance estimator using sieve approximation theory and empirical process theory. The proposed test is computationally feasible regardless of the number of independent variables in the imputation model. We demonstrate the advantages of the proposed method over existing methods through extensive simulation studies and provide an application to a major cancer genomics study. Supplementary materials for this article are available online.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of the American Statistical Association, 2019, v. 114, no. 528, p. 1778-1786-
dcterms.isPartOfJournal of the American Statistical Association-
dcterms.issued2019-
dc.identifier.eissn1537-274X-
dc.description.validate202004 bcrc-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0431-n01en_US
dc.description.pubStatusPublisheden_US
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