Lunch Seminar MESIO UPC-UB

Speaker:

Abstract:

The aim of this presentation is to review joint modelling techniques for the simultaneous analysis of time‐to‐event data and longitudinal time‐varying data. This is an increasing area of interest in both the methodological and the applied point of view and it allows the analysis and understanding of complex systems.

Among others, three main advantages of this approach are: a) it corrects the bias derived from a traditional separate analysis, b) the modelization allows to incorporate and model the between and within correlation among observations and, c) true longitudinal profiles for endogenous covariates can be included in the relative hazard survival sub‐model.

The relevant benefit of these models is being able to estimate the effect of each subject‐specific longitudinal profile in the hazard function for the event of interest, in an adaptive manner. In particular, subject‐specific dynamic predictions, like conditional survival functions given the available longitudinal information, can be derived.

Estimation procedure and implementation in R (JM and JMbayes packages) will be introduced and some illustrations will be given. Extensions to the case of multiple time-to-event variables with multiple longitudinal covariates will be also considered.