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Joint Modeling of a Longitudinal Measurement and parametric survival data with application to primary biliary cirrhosis study

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dc.contributor.author Dil, Elif
dc.contributor.author Karasoy, Duru
dc.date.accessioned 2021-01-12T08:59:33Z
dc.date.available 2021-01-12T08:59:33Z
dc.date.issued 2020-06
dc.identifier.issn 1816-2711
dc.identifier.uri https://doi.org/10.18187/pjsor.v16i2.3131
dc.identifier.uri http://hdl.handle.net/20.500.12485/696
dc.description.abstract Although longitudinal and survival data are collected in the same study, they are usually analyzed separately. Measurement errors and missing data problems arise because of separate analysis of these two data. Therefore, joint model should be used instead of separate analysis. The standard joint model frequently used in the literature is obtained by combining the linear mixed effect model of longitudinal data and Cox regression model with survival data. Nevertheless, to use the Cox regression model for survival data, the assumption of proportional hazards must be provided. Parametric survival sub-models should be used instead of the Cox regression model for the survival sub-model of the JM where the assumption is not provided. In this article, parametric joint modeling of longitudinal data and survival data that do not provide the assumption of proportional hazards are investigated. For the survival data, the model with Exponential, Weibull, Log-normal, Log-logistic, and Gamma accelerated failure time models and the linear mixed effect model are combined with random effects and the models were applied in primary biliary cirrhosis data set obtained from Mayo Clinic. After determining the best parametric joint model according to Akaike and Bayesian information criterions, the best available model was compared with standard joint model and of separate analysis of survival data and longitudinal data. As a result, in the studies where longitudinal and survival data are obtained together, it is seen that the parametric joint model gives more better results than the standard joint model when the proportional hazard assumption is not provided. en_US
dc.language.iso en en_US
dc.publisher UNIV PUNJAB, INST GEOL, QUID-E-AZAM CAMPUS, PO BOX NO 54590, LAHORE, 00000, PAKISTAN en_US
dc.subject Accelerated failure time models en_US
dc.subject Longitudinal data en_US
dc.subject Parametric joint modelling en_US
dc.subject Standard joint modelling en_US
dc.subject Survival data en_US
dc.title Joint Modeling of a Longitudinal Measurement and parametric survival data with application to primary biliary cirrhosis study en_US
dc.type Article en_US
dc.relation.journal PAKISTAN JOURNAL OF STATISTICS AND OPERATION RESEARCH en_US
dc.identifier.issue 2 en_US
dc.identifier.startpage 295 en_US
dc.identifier.endpage 304 en_US
dc.identifier.volume 16 en_US


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