A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes

• Published in: Structural equation modeling Vol. 27; no. 3; pp. 442 - 467
• Main Authors: Ji, Linying, Chen, Meng, Oravecz, Zita, Cummings, E. Mark, Lu, Zhao-Hua, Chow, Sy-Miin
• Format: Journal Article
• Language: English
• Published: United States Routledge 03.05.2020; Psychology Press
• Subjects: Bayesian analysis; Bayesian vector autoregressive model; Dependent variables; Intensive longitudinal data; Missing data; Multiple imputation; Nonignorable missing data; Time series; Intensive longitudinal data; Bayesian vector autoregressive model; Multiple imputation; Nonignorable missing data
• ISSN: 1070-5511; 1532-8007
• DOI: 10.1080/10705511.2019.1623681

Intensive longitudinal designs involving repeated assessments of constructs often face the problems of nonignorable attrition and selected omission of responses on particular occasions. However, time series models, such as vector autoregressive (VAR) models, are often fit to these data without consideration of nonignorable missingness. We introduce a Bayesian model that simultaneously represents the over-time dependencies in multivariate, multiple-subject time series data via a VAR model, and possible ignorable and nonignorable missingness in the data. We provide software code for implementing this model with application to an empirical data set. Moreover, simulation results comparing the joint approach with two-step multiple imputation procedures are included to shed light on the relative strengths and weaknesses of these approaches in practical data analytic scenarios.