  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.
