When a joint model should be preferred over a linear mixed model for analysis of longitudinal health-related quality of life data in cancer clinical trials.
Fiche publication
Date publication
février 2023
Journal
BMC medical research methodology
Auteurs
Membres identifiés du Cancéropôle Est :
Pr CONROY Thierry
Tous les auteurs :
Touraine C, Cuer B, Conroy T, Juzyna B, Gourgou S, Mollevi C
Lien Pubmed
Résumé
Patient-reported outcomes such as health-related quality of life (HRQoL) are increasingly used as endpoints in randomized cancer clinical trials. However, the patients often drop out so that observation of the HRQoL longitudinal outcome ends prematurely, leading to monotone missing data. The patients may drop out for various reasons including occurrence of toxicities, disease progression, or may die. In case of informative dropout, the usual linear mixed model analysis will produce biased estimates. Unbiased estimates cannot be obtained unless the dropout is jointly modeled with the longitudinal outcome, for instance by using a joint model composed of a linear mixed (sub)model linked to a survival (sub)model. Our objective was to investigate in a clinical trial context the consequences of using the most frequently used linear mixed model, the random intercept and slope model, rather than its corresponding joint model.
Mots clés
Cancer, Clinical trials, Health-related quality of life, Informative dropout, Joint model, Linear mixed model, Longitudinal outcome, Random intercept and slope model
Référence
BMC Med Res Methodol. 2023 02 10;23(1):36