Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations.

ABSTRACT: Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects.

SUBMITTER: Rustand D 

PROVIDER: S-EPMC11017128 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature