A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes
A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes
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DOI：
10.1080/10705511.2019.1623681
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Linying Ji;Meng Chen;Zita Oravecz;E. Mark Cummings;Zhao-Hua Lu;Sy-Miin Chow
中科院分区：
文献类型：
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作者：
Linying Ji;Meng Chen;Zita Oravecz;E. Mark Cummings;Zhao-Hua Lu;Sy-Miin Chow
密集的纵向设计，包括重复评估的结构往往面临的问题，不可替代的磨损和选择性遗漏的反应，在特定场合。然而，时间序列模型，如向量自回归（VAR）模型，往往是适合这些数据，而不考虑不可分割的缺失。我们引入了一个贝叶斯模型，同时表示在多变量，多主题的时间序列数据，通过VAR模型的时间依赖性，以及可能的可验证和不可验证的数据中的缺失。我们提供的软件代码实现这个模型的应用经验数据集。此外，模拟结果比较联合方法与两步多重插补程序，揭示了这些方法在实际数据分析方案的相对优势和劣势。
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.