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dc.contributor.authorChen, Bingshu
dc.contributor.authorWang, Jia
dc.date.accessioned2020-04-13T13:42:34Z
dc.date.available2020-04-13T13:42:34Z
dc.date.issued2020-02-10
dc.identifier.citationBingshu E. Chen. and Jia Wang (2020). Joint Modeling of Binary Response and Survival for Clustered data in Clinical Trials. Statistics in Medicine. vol 39, page 326-339.en
dc.identifier.urihttp://hdl.handle.net/1974/27695
dc.descriptionPublished as: Chen, Bingshu E; Wang, Jia, Joint modeling of binary response and survival for clustered data in clinical trials in Statistics in Medicine, 2020, 39(3): 326-339 https://doi.org/10.1002/sim.8403en
dc.description.abstractIn clinical trials, it is often desirable to evaluate the effect of a prognostic factor such as a marker response on a survival outcome. However, the marker response and survival outcome are usually associated with some potentially unobservable factors. In this case, the conventional statistical methods that model these two outcomes separately may not be appropriate. In this paper, we propose a joint model for marker response and survival outcomes for clustered data, providing efficient statistical inference by considering these two outcomes simultaneously. We focus on a special type of marker response: a binary outcome, which is investigated together with survival data using a cluster-specific multivariate random effect variable. A multivariate penalized likelihood method is developed to make statistical inference for the joint model. However, the standard errors obtained from the penalized likelihood method are usually underestimated. This issue is addressed using a Jackknife resampling method to obtain a consistent estimate of standard errors. We conduct extensive simulation studies to assess the finite sample performance of the proposed joint model and inference methods in different scenarios. The simulation studies show that the proposed joint model has excellent finite sample properties compared to the separate models when there exists an underlying association between the marker response and survival data. Finally, we apply the proposed method to a symptom control study conducted by Canadian Cancer Trials Group to explore the prognostic effect of covariates on pain control and overall survival.en
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canadaen
dc.language.isoenen
dc.publisherWileyen
dc.subjectGeneralized Linear Mixed Modelen
dc.subjectLaplace Transformationen
dc.subjectJackknifeen
dc.subjectMarker Responseen
dc.subjectPenalized Likelihooden
dc.subjectRandom Effects Modelen
dc.subjectSurvival Analysisen
dc.titleJoint Modeling of Binary Response and Survival for Clustered data in Clinical Trialsen
dc.typejournal articleen
dc.identifier.doihttps://doi.org/10.1002/sim.8403


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