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

Hok Pan Yuen; Andrew Mackinnon

doi:10.7717/peerj.2582

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

PeerJ. 2016 Oct 19:4:e2582. doi: 10.7717/peerj.2582. eCollection 2016.

Authors

Hok Pan Yuen¹, Andrew Mackinnon²

Affiliations

¹ Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia.
² Centre for Mental Health, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia; Black Dog Institute and University of New South Wales, Sydney, New South Wales, Australia.

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 conventional Cox regression analysis. We also compared a number of packages for fitting joint models. Our results suggest that joint modelling do have advantages over conventional analysis despite its potential complexity. Our results also suggest that the results of analyses may depend on how the methodology is implemented.

Keywords: Joint modelling; Simulations; Software packages; Time-to-event outcome; Transition to psychosis.

Grants and funding

The authors received no funding for this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.