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

PeerJ

article

Hok Pan Yuen¹  Andrew Mackinnon³ 

[1] Orygen, The National Centre of Excellence in Youth Mental Health;Centre for Youth Mental Health, The University of Melbourne;Centre for Mental Health, Melbourne School of Population and Global Health, The University of Melbourne;Black Dog Institute and University of New South Wales

关键词: Joint modelling;    Simulations;    Software packages;    Time-to-event outcome;    Transition to psychosis;   

DOI  :  10.7717/peerj.2582

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY