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dc.contributor.authorTchetgen, Eric J Tchetgenen_US
dc.date.accessioned2015-02-02T15:32:26Z
dc.date.issued2014en_US
dc.identifier.citationTchetgen, Eric J Tchetgen. 2014. “Identification and estimation of survivor average causal effects.” Statistics in Medicine 33 (21): 3601-3628. doi:10.1002/sim.6181. http://dx.doi.org/10.1002/sim.6181.en
dc.identifier.issn0277-6715en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:13890654
dc.description.abstractIn longitudinal studies, outcomes ascertained at follow-up are typically undefined for individuals who die prior to the follow-up visit. In such settings, outcomes are said to be truncated by death and inference about the effects of a point treatment or exposure, restricted to individuals alive at the follow-up visit, could be biased even if as in experimental studies, treatment assignment were randomized. To account for truncation by death, the survivor average causal effect (SACE) defines the effect of treatment on the outcome for the subset of individuals who would have survived regardless of exposure status. In this paper, the author nonparametrically identifies SACE by leveraging post-exposure longitudinal correlates of survival and outcome that may also mediate the exposure effects on survival and outcome. Nonparametric identification is achieved by supposing that the longitudinal data arise from a certain nonparametric structural equations model and by making the monotonicity assumption that the effect of exposure on survival agrees in its direction across individuals. A novel weighted analysis involving a consistent estimate of the survival process is shown to produce consistent estimates of SACE. A data illustration is given, and the methods are extended to the context of time-varying exposures. We discuss a sensitivity analysis framework that relaxes assumptions about independent errors in the nonparametric structural equations model and may be used to assess the extent to which inference may be altered by a violation of key identifying assumptions. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.en
dc.language.isoen_USen
dc.publisherBlackWell Publishing Ltden
dc.relation.isversionofdoi:10.1002/sim.6181en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4131726/pdf/en
dash.licenseLAAen_US
dc.subjecttruncation by deathen
dc.subjectprincipal stratificationen
dc.subjectdouble robusten
dc.subjectsensitivity analysisen
dc.titleIdentification and estimation of survivor average causal effectsen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalStatistics in Medicineen
dc.date.available2015-02-02T15:32:26Z
dc.identifier.doi10.1002/sim.6181*


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