BCAM Scientific Seminar: A novel two-stage approach for joint models of longitudinal and survival data

Abstract: 
In medical research, the variables of interest are usually the moment of occurrence of an event, such as clinical diagnosis, cure of a disease or death; and/or an unobserved latent process measured repeatedly over time, as an indicator of the progression of a certain disease. In some applications, both the time until an event of interest and the longitudinal observations are available. In these cases, joint modelling is required, since a separate analysis may lead to inefficient or biased results. From the 90s onwards, several authors have proposed joint models for follow-up data, where typically survival studies incorporate the effect of an endogenous time-dependent covariate measured with error, and longitudinal analysis corrects the nonrandom dropout with a time-to-event model. In this talk, we introduce the main mathematical formulation for a joint model of longitudinal and survival data and discuss its standard estimation approaches. In addition, we present a novel two-stage estimation methodology based on simulation-extrapolation techniques. Simulation data are used to compare the accuracy and computational time of each approach.

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