Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference.



Barrett, Jessica, Diggle, Peter ORCID: 0000-0003-3521-5020, Henderson, Robin and Taylor-Robinson, David ORCID: 0000-0002-5828-7724
(2015) Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference. Journal of the Royal Statistical Society. Series B, Statistical methodology, 77 (1). 131 - 148.

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Abstract

Random effects or shared parameter models are commonly advocated for the analysis of combined repeated measurement and event history data, including dropout from longitudinal trials. Their use in practical applications has generally been limited by computational cost and complexity, meaning that only simple special cases can be fitted by using readily available software. We propose a new approach that exploits recent distributional results for the extended skew normal family to allow exact likelihood inference for a flexible class of random-effects models. The method uses a discretization of the timescale for the time-to-event outcome, which is often unavoidable in any case when events correspond to dropout. We place no restriction on the times at which repeated measurements are made. An analysis of repeated lung function measurements in a cystic fibrosis cohort is used to illustrate the method.

Item Type: Article
Depositing User: Symplectic Admin
Date Deposited: 27 Jan 2015 15:58
Last Modified: 02 Oct 2021 07:15
DOI: 10.1111/rssb.12060
Publisher's Statement : © 2014 TheAuthors Journal of the Royal Statistical Society:Series B(Statistical Methodology) Published by JohnWiley&Sons Ltd on behalf of the Royal Statistical Society. This is an open access article under the terms of the CreativeCommonsAttribution License, which permits use, distribution and reproduction in any medium, provided the originalwork is properly cited.
URI: https://livrepository.liverpool.ac.uk/id/eprint/2005879