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

Barrett, Jessica and Diggle, Peter John and Henderson, Oliver Robin and Taylor-Robinson, David (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). pp. 131-148. ISSN 1369-7412

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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:
Journal Article
Journal or Publication Title:
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
82732
Deposited By:
Deposited On:
08 Nov 2016 14:18
Refereed?:
Yes
Published?:
Published
Last Modified:
30 May 2020 04:55