Performance of joint modelling of time-to-event data with time-dependent predictors: an assessment based on transition to psychosis data.

ABSTRACT: Joint modelling has emerged to be a potential tool to analyse data with a time-to-event outcome and longitudinal measurements collected over a series of time points. Joint modelling involves the simultaneous modelling of the two components, namely the time-to-event component and the longitudinal component. The main challenges of joint modelling are the mathematical and computational complexity. Recent advances in joint modelling have seen the emergence of several software packages which have implemented some of the computational requirements to run joint models. These packages have opened the door for more routine use of joint modelling. Through simulations and real data based on transition to psychosis research, we compared joint model analysis of time-to-event outcome with the convention

SUBMITTER: Yuen HP 

PROVIDER: S-EPMC5075698 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature