Joint modelling of longitudinal data: a scoping review of methodology and applications for non-time to event data.

ABSTRACT:

Background

Joint models are powerful statistical models that allow us to define a joint likelihood for quantifying the association between two or more outcomes. Joint modelling has been shown to reduce bias in parameter estimates, increase the efficiency of statistical inference by incorporating the correlation between measurements, and allow borrowing of information in cases where data is missing for variables of interest. Most joint modelling methods and applications involve time-to-event data. There is less awareness about the amount of literature available for joint models of non-time-to-event data. Therefore, this review's main objective is to summarise the current state of joint modelling of non-time-to-event longitudinal data.

Methods

We conducted a search in PubMed,

SUBMITTER: Ouko RK 

PROVIDER: S-EPMC11831847 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature