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dc.contributor.authorCook, Richard J.
dc.contributor.authorBoruvka, Audrey 14:44:22 (GMT) 14:44:22 (GMT)
dc.descriptionThis is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The version of record Boruvka, Audrey and Cook, Richard J. (2016). Biostatistics, 17(2): 350-363. DOI: 10.1093/biostatistics/kxv042 is available online at:
dc.description.abstractSemiparametric methods are well-established for the analysis of a progressive Markov illness-death process observed up to a noninformative right censoring time. However often the intermediate and terminal events are censored in different ways, leading to a dual censoring scheme. In such settings unbiased estimation of the cumulative transition intensity functions cannot be achieved without some degree of smoothing. To overcome this problem we develop a sieve maximum likelihood approach for inference on the hazard ratio. A simulation study shows that the sieve estimator offers improved finite-sample performance over common imputation-based alternatives and is robust to some forms of dependent censoring. The proposed method is illustrated using data from cancer trials.en
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626)en
dc.publisherOxford Journalsen
dc.subjectCox modelen
dc.subjectInterval censoringen
dc.subjectMethod of sievesen
dc.subjectProfile likelihooden
dc.subjectProgression-free survivalen
dc.titleSieve estimation in a Markov illness-death process under dual censoringen
dcterms.bibliographicCitationAudrey Boruvka and Richard J. Cook Sieve estimation in a Markov illness-death process under dual censoring Biostat. 2016 17: 350-363.en
uws.contributor.affiliation1Faculty of Mathematicsen
uws.contributor.affiliation2Statistics and Actuarial Scienceen

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