Joint Models of Longitudinal and Time-to-Event Data with More Than One Event Time Outcome: A Review

Hickey Graeme L., Philipson Pete, Jorgensen Andrea and Kolamunnage-Dona Ruwanthi ()
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Hickey Graeme L.: Department of Biostatistics,University of Liverpool, Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK
Philipson Pete: Department of Mathematics,Physics and Electrical Engineering, Northumbria University, Ellison Place, Newcastle upon Tyne, NE1 8ST, UK
Jorgensen Andrea: Department of Biostatistics,University of Liverpool, Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK
Kolamunnage-Dona Ruwanthi: Department of Biostatistics,University of Liverpool, Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK

The International Journal of Biostatistics, 2018, vol. 14, issue 1, 19

Abstract: Methodological development and clinical application of joint models of longitudinal and time-to-event outcomes have grown substantially over the past two decades. However, much of this research has concentrated on a single longitudinal outcome and a single event time outcome. In clinical and public health research, patients who are followed up over time may often experience multiple, recurrent, or a succession of clinical events. Models that utilise such multivariate event time outcomes are quite valuable in clinical decision-making. We comprehensively review the literature for implementation of joint models involving more than a single event time per subject. We consider the distributional and modelling assumptions, including the association structure, estimation approaches, software implementations, and clinical applications. Research into this area is proving highly promising, but to-date remains in its infancy.

Keywords: Joint models; multivariate data; longitudinal data; time-to-event data; recurrent events (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1515/ijb-2017-0047

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