Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data.

ABSTRACT:

Background

In clinical trials and epidemiological research, mixed-effects models are commonly used to examine population-level and subject-specific trajectories of biomarkers over time. Despite their increasing popularity and application, the specification of these models necessitates a great deal of care when analysing longitudinal data with non-linear patterns and asymmetry. Parametric (linear) mixed-effect models may not capture these complexities flexibly and adequately. Additionally, assuming a Gaussian distribution for random effects and/or model errors may be overly restrictive, as it lacks robustness against deviations from symmetry.

Methods

This paper presents a semiparametric mixed-effects model with flexible distributions for complex longitudinal data in the Bayes

SUBMITTER: Ferede MM 

PROVIDER: S-EPMC10908071 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature