TY - JOUR
T1 - A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates
T2 - Development, Evaluation, and Application to Family Processes
AU - Ji, Linying
AU - Chen, Meng
AU - Oravecz, Zita
AU - Cummings, E. Mark
AU - Lu, Zhao Hua
AU - Chow, Sy Miin
PY - 2020/5/3
Y1 - 2020/5/3
N2 - 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.
AB - 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.
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U2 - 10.1080/10705511.2019.1623681
DO - 10.1080/10705511.2019.1623681
M3 - Article
C2 - 32601517
AN - SCOPUS:85084696122
VL - 27
SP - 442
EP - 467
JO - Structural Equation Modeling
JF - Structural Equation Modeling
SN - 1070-5511
IS - 3
ER -