Joint Models for Discrete Longitudinal Outcomes in Aging Research

Abstract

Summary Given the aging population in the UK, statistical modelling of cognitive function in the older population is of interest. Joint models are formulated for survival and cognitive function in the older population. Because tests of cognitive function often result in discrete outcomes, binomial and beta–binomial mixed effects regression models are applied to analyse longitudinal measurements. Dropout due to death is accounted for by parametric survival models, where the choice of a Gompertz baseline hazard and the specification of the random‐effects structure are of specific interest. The measurement model and the survival model are combined in a shared parameter joint model. Estimation is by marginal likelihood. The methods are used to analyse data from the Cambridge City over‐75s cohort study and the English Longitudinal Study of Ageing.

Shared parameter models for joint analysis of longitudinal and survival data with left truncation due to delayed entry – Applications to cystic fibrosis

“…When analyzing time-to-event data, methods for handling left truncated survival data in the parametric and semi- or nonparametric setting are well developed 5-7 . Although methods for analysis of longitudinal data of a serial biomarker when follow up is left truncated has received less attention, recent papers have shown how it can be facilitated using joint modeling techniques 8-12 . Proust-Lima et al 11 and Dantan et al 9 developed joint modeling approaches incorporating left truncation in the framework of latent class models and mulitistate models, respectively, and applied them to studies of cognitive decline in the elderly.…”

“…5–7 Although methods for analysis of longitudinal data of a serial biomarker when follow-up is left truncated has received less attention, recent papers have shown how it can be facilitated using joint modeling techniques. 8–12 Proust-Lima et al. 11 and Dantan et al.…”

“…9 developed joint modeling approaches incorporating left truncation in the framework of latent class models and mulitistate models, respectively, and applied them to studies of cognitive decline in the elderly. van den Hout and Muniz-Terrera 12 developed a shared parameter model for a survival and a discrete ordinal longitudinal outcome and also applied the methods to studies of cognitive decline in a longitudinal study of ageing. Crowther et al.…”

“…[5][6][7] Although methods for analysis of longitudinal data of a serial biomarker when follow-up is left truncated has received less attention, recent papers have shown how it can be facilitated using joint modeling techniques. [8][9][10][11][12] Proust-Lima et al 11 and Dantan et al 9 developed joint modeling approaches incorporating left truncation in the framework of latent class models and mulitistate models, respectively, and applied them to studies of cognitive decline in the elderly. van den Hout and Muniz-Terrera 12 developed a shared parameter model for a survival and a discrete ordinal longitudinal outcome and also applied the methods to studies of cognitive decline in a longitudinal study of ageing.…”

Joint modelling of longitudinal and survival data: incorporating delayed entry and an assessment of model misspecification

“…They assumed a linear mixed effects model for the longitudinal outcome, with random intercept and slope, which, along with a transformation of age at death, were assumed to follow a trivariate normal distribution. Van den Hout and Muniz-Terrera [19] developed a joint model for repeatedly measured discrete data, arising from tests of cognitive function, and survival in the analysis of an older population. They allowed for a random intercept and linear slope in the longitudinal submodel, assessing a binomial or beta-binomial formulation, and a Weibull or Gompertz survival model.…”