Joint Modelling for Longitudinal and Time-to-Event Data: Application to Liver Transplantation Data
نویسندگان
چکیده
A common objective in follow-up studies is to characterize the relationship between longitudinal measurements and time-to-event outcomes. For this aim, various methods were proposed in the statistical literature, such as an extended version of the Cox model with longitudinal covariates or a two-stage approach. However, these techniques have several limitations, including the possibility of biased estimations. To avoid these limitations, joint modelling approaches are becoming increasingly popular. In this work, we provide a brief overview of a joint model approach for longitudinal and time-to-event data, focusing on the survival process. Also, the predictive capacity of this model is studied and related computational aspects, including available software, are discussed. The main motivation behind this work relies on the application of the joint modelling to liver transplantation data, in order to investigate the abilities of postoperative glucose profiles to predict patients’ survival.
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