Bayesian analysis of longitudinal dyadic data with informative missing data using a dyadic shared-parameter model.

ABSTRACT: Analyzing longitudinal dyadic data is a challenging task due to the complicated correlations from repeated measurements and within-dyad interdependence, as well as potentially informative (or non-ignorable) missing data. We propose a dyadic shared-parameter model to analyze longitudinal dyadic data with ordinal outcomes and informative intermittent missing data and dropouts. We model the longitudinal measurement process using a proportional odds model, which accommodates the within-dyad interdependence using the concept of the actor-partner interdependence effects, as well as dyad-specific random effects. We model informative dropouts and intermittent missing data using a transition model, which shares the same set of random effects as the longitudinal measurement model. We evaluate the pe

SUBMITTER: Ahn J 

PROVIDER: S-EPMC5568500 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature