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Title: Joint longitudinal and time-to-event models for multilevel hierarchical data
Authors: Brilleman, SL
Crowther, MJ
Moreno-Betancur, M
Buros Novik, J
Dunyak, J
Al-Huniti, N
Fox, R
Hammerbacher, J
Wolfe, R
First Published: 31-Oct-2018
Publisher: SAGE Publications
Citation: Stat Methods Med Res, 2018, pp. 962280218808821-?
Abstract: Joint modelling of longitudinal and time-to-event data has received much attention recently. Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper, we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progression-free survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient. Since a patient may have more than one lesion, and each lesion is tracked over time, the data have a three-level hierarchical structure: repeated measurements taken at time points (level 1) clustered within lesions (level 2) within patients (level 3). We jointly model the lesion-specific longitudinal trajectories and patient-specific risk of death or disease progression by specifying novel association structures that combine information across lower level clusters (e.g. lesions) into patient-level summaries (e.g. tumor burden). We provide user-friendly software for fitting the model under a Bayesian framework. Lastly, we discuss alternative situations in which additional clustering factor(s) occur at a level higher in the hierarchy than the patient-level, since this has implications for the model formulation.
DOI Link: 10.1177/0962280218808821
eISSN: 1477-0334
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2018, The Authors. Deposited with reference to the publisher’s open access archiving policy. (
Appears in Collections:Published Articles, Dept. of Health Sciences

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