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dc.contributor.authorYuen, HP
dc.contributor.authorMackinnon, A
dc.date.accessioned2021-02-12T00:22:35Z
dc.date.available2021-02-12T00:22:35Z
dc.date.issued2016-10-19
dc.identifierpii: 2582
dc.identifier.citationYuen, H. P. & Mackinnon, A. (2016). Performance of joint modelling of time-to-event data with time-dependent predictors: an assessment based on transition to psychosis data. PEERJ, 4 (10), https://doi.org/10.7717/peerj.2582.
dc.identifier.issn2167-8359
dc.identifier.urihttp://hdl.handle.net/11343/260568
dc.description.abstractJoint 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.
dc.languageEnglish
dc.publisherPEERJ INC
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titlePerformance of joint modelling of time-to-event data with time-dependent predictors: an assessment based on transition to psychosis data
dc.typeJournal Article
dc.identifier.doi10.7717/peerj.2582
melbourne.affiliation.departmentCentre for Youth Mental Health
melbourne.affiliation.departmentMelbourne School of Population and Global Health
melbourne.affiliation.facultyMedicine, Dentistry & Health Sciences
melbourne.source.titlePeerJ
melbourne.source.volume4
melbourne.source.issue10
dc.rights.licenseCC BY
melbourne.elementsid1113738
melbourne.contributor.authorYuen, Hok
melbourne.contributor.authorMacKinnon, Andrew
dc.identifier.eissn2167-8359
melbourne.accessrightsOpen Access


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