Joint Modelling of Survival and Longitudinal Data under Nested Case-control Sampling

Abstract

In a cohort study, subjects are followed-up over a long period. In addition to baseline characteristics, often longitudinal measurements thought to be important predictors of survival are collected at discrete time points for all or a subset of cohort members. Joint modeling of survival and longitudinal data is commonly used to overcome the issue that the actual longitudinal measurement at time of event is often unknown due to its discrete nature. Few studies have investigated the use of joint modeling under the nested case-control sampling, despite the great potential of cost savings. In this thesis, we compared a published maximum likelihood estimation (MLE) method and our proposed weighted MLE method. We applied both methods to a simulation study and real data and found that the estimated values for both are almost similar, but our proposed method can be used when only information on those selected into the study is available.