Open Access
Translator Disclaimer
June 2012 Bayesian modeling longitudinal dyadic data with nonignorable dropout, with application to a breast cancer study
Guangyu Zhang, Ying Yuan
Ann. Appl. Stat. 6(2): 753-771 (June 2012). DOI: 10.1214/11-AOAS515

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.

Citation

Download Citation

Guangyu Zhang. Ying Yuan. "Bayesian modeling longitudinal dyadic data with nonignorable dropout, with application to a breast cancer study." Ann. Appl. Stat. 6 (2) 753 - 771, June 2012. https://doi.org/10.1214/11-AOAS515

Information

Published: June 2012
First available in Project Euclid: 11 June 2012

zbMATH: 1243.62039
MathSciNet: MR2976490
Digital Object Identifier: 10.1214/11-AOAS515

Keywords: Dyadic data , missing data , nonignorable dropout , selection model

Rights: Copyright © 2012 Institute of Mathematical Statistics

JOURNAL ARTICLE
19 PAGES


SHARE
Vol.6 • No. 2 • June 2012
Back to Top