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

Jessica Barrett, Peter Diggle, Robin Henderson, David Taylor-Robinson

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)131-148
Number of pages18
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume77
Issue number1
DOIs
StatePublished - Jan 1 2015
Externally publishedYes

Keywords

  • Cystic fibrosis
  • Dropout
  • Joint modelling
  • Repeated measurements
  • Skew normal distribution
  • Survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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