Bayesian modeling longitudinal dyadic data with nonignorable dropout, with application to a breast cancer study

TL;DR

This paper introduces a Bayesian approach for analyzing longitudinal dyadic data with nonignorable dropouts, accounting for interdependence within dyads and dropout mechanisms, demonstrated through a breast cancer study.

Contribution

It develops a fully Bayesian selection-model-based method with a Gibbs sampler to handle nonignorable dropout in dyadic longitudinal data, a novel approach in this context.

Findings

Effective in addressing nonignorable dropouts in simulations

Successfully applied to a breast cancer longitudinal study

Improves accuracy of dyadic data analysis with missing data

Abstract

Dyadic data are common in the social and behavioral sciences, in which members of dyads are correlated due to the interdependence structure within dyads. The analysis of longitudinal dyadic data becomes complex when nonignorable dropouts occur. We propose a fully Bayesian selection-model-based approach to analyze longitudinal dyadic data with nonignorable dropouts. We model repeated measures on subjects by a transition model and account for within-dyad correlations by random effects. In the model, we allow subject's outcome to depend on his/her own characteristics and measure history, as well as those of the other member in the dyad. We further account for the nonignorable missing data mechanism using a selection model in which the probability of dropout depends on the missing outcome. We propose a Gibbs sampler algorithm to fit the model. Simulation studies show that the proposed method effectively addresses the problem of nonignorable dropouts. We illustrate our methodology using a longitudinal breast cancer study.