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

• 成果类型: Article
• 署名作者: Barrett, Jessica; Diggle, Peter; Henderson, Robin; Taylor-Robinson, David
• 署名单位: University of Cambridge; Lancaster University; University of Liverpool; Newcastle University - UK
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12060
• 发表日期: 2015
• 页码: 131-148
• 关键词: cystic-fibrosis; survival; decline

摘要

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.