Joint modeling of cross-sectional health outcomes and longitudinal predictors via mixtures of means and variances.

ABSTRACT: Joint modeling methods have become popular tools to link important features extracted from longitudinal data to a primary event. While most modeling strategies have focused on the association between the longitudinal mean trajectories and risk of an event, we consider joint models that incorporate information from both long-term trends and short-term variability in a longitudinal submodel. We also consider both shared random effect and latent class (LC) approaches in the primary-outcome model to predict a binary outcome of interest. We develop simulation studies to compare and contrast these two modeling strategies; in particular, we study in detail the effects of the primary-outcome model misspecification. Among other findings, we note that when we analyze data from a shared random-effect

SUBMITTER: Jiang B 

PROVIDER: S-EPMC4480207 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature