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dc.contributor.authorLiao, Xiaomei
dc.contributor.authorZucker, David M.
dc.contributor.authorLi, Yi
dc.contributor.authorSpiegelman, Donna
dc.date.accessioned2019-09-21T16:11:56Z
dc.date.issued2011
dc.identifier.citationLiao, Xiaomei, David M. Zucker, Yi Li, and Donna Spiegelman. 2010. “Survival Analysis with Error-Prone Time-Varying Covariates: A Risk Set Calibration Approach.” Biometrics 67 (1): 50–58. https://doi.org/10.1111/j.1541-0420.2010.01423.x.
dc.identifier.issn0006-341X
dc.identifier.issn1541-0420
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41384746*
dc.description.abstractOccupational, environmental, and nutritional epidemiologists are often interested in estimating the prospective effect of time-varying exposure variables such as cumulative exposure or cumulative updated average exposure, in relation to chronic disease endpoints such as cancer incidence and mortality. From exposure validation studies, it is apparent that many of the variables of interest are measured with moderate to substantial error. Although the ordinary regression calibration (ORC) approach is approximately valid and efficient for measurement error correction of relative risk estimates from the Cox model with time-independent point exposures when the disease is rare, it is not adaptable for use with time-varying exposures. By recalibrating the measurement error model within each risk set, a risk set regression calibration (RRC) method is proposed for this setting. An algorithm for a bias-corrected point estimate of the relative risk using an RRC approach is presented, followed by the derivation of an estimate of its variance, resulting in a sandwich estimator. Emphasis is on methods applicable to the main study/external validation study design, which arises in important applications. Simulation studies under several assumptions about the error model were carried out, which demonstrated the validity and efficiency of the method in finite samples. The method was applied to a study of diet and cancer from Harvard's Health Professionals Follow-up Study (HPFS).
dc.language.isoen_US
dc.publisherWiley
dash.licenseLAA
dc.titleSurvival analysis with error-prone time-varying covariates: a risk set calibration approach
dc.typeJournal Article
dc.description.versionAccepted Manuscript
dc.relation.journalBiometrics
dash.depositing.authorSpiegelman, Donna::37eeac21962b33e4e46e7aedde542849::600
dc.date.available2019-09-21T16:11:56Z
dash.workflow.comments1Science Serial ID 14800
dc.identifier.doi10.1111/j.1541-0420.2010.01423.x
dash.source.volume67;1
dash.source.page50


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