Interpretation of mixed models and marginal models with cohort attrition due to death and drop-out. (February 2019)

• Record Type: Journal Article
• Authors: Rouanet, Anaïs; Helmer, Catherine; Dartigues, Jean-François; Jacqmin-Gadda, Hélène
• Is Part Of: Statistical methods in medical research. Volume 28:Number 2(2019)
• Page Start: 343
• Page End: 356
• Publication Date: 2019-02
• Subjects: Death -- immortal cohort -- GEE -- mixed models -- partly conditional -- population-averaged -- subject-specific; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217723675
• Languages: English
• ISSNs: 0962-2802

Abstract

Mixed models estimated by maximum likelihood and marginal models estimated by generalized estimating equations are the standard methods for the analysis of longitudinal data. However, their use is highly debated when attrition may be due to death. While some authors consider that mixed model estimates are interpretable only in an immortal cohort, we show that their subject-specific interpretation still holds in the population currently alive, but their population-averaged interpretation is valid only in the immortal cohort. We propose an approximation of the population-averaged mean among the population alive that highlights the difference with the population-averaged mean in the immortal cohort. The interpretation of ML estimates of mixed models and joint models for the marker and the time-to-death as well as unweighted and weighted GEE of marginal models is then illustrated in a simulation study and in an application regarding cognitive decline in the elderly.