A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes
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 consi...
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| Published in | Structural equation modeling Vol. 27; no. 3; pp. 442 - 467 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Routledge
03.05.2020
Psychology Press |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1070-5511 1532-8007 |
| DOI | 10.1080/10705511.2019.1623681 |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Cummings, E. Mark Lu, Zhao-Hua Chen, Meng Chow, Sy-Miin Ji, Linying Oravecz, Zita |
| AuthorAffiliation | 1 The Pennsylvania State University 2 University of Notre Dame 3 St. Jude Children’s Research Hospital |
| AuthorAffiliation_xml | – name: 1 The Pennsylvania State University – name: 2 University of Notre Dame – name: 3 St. Jude Children’s Research Hospital |
| Author_xml | – sequence: 1 givenname: Linying surname: Ji fullname: Ji, Linying email: lzj114@psu.edu organization: The Pennsylvania State University – sequence: 2 givenname: Meng surname: Chen fullname: Chen, Meng organization: The Pennsylvania State University – sequence: 3 givenname: Zita surname: Oravecz fullname: Oravecz, Zita organization: The Pennsylvania State University – sequence: 4 givenname: E. Mark surname: Cummings fullname: Cummings, E. Mark organization: University of Notre Dame – sequence: 5 givenname: Zhao-Hua surname: Lu fullname: Lu, Zhao-Hua organization: St. Jude Children's Research Hospital – sequence: 6 givenname: Sy-Miin surname: Chow fullname: Chow, Sy-Miin organization: The Pennsylvania State University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32601517$$D View this record in MEDLINE/PubMed |
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| Keywords | Intensive longitudinal data Bayesian vector autoregressive model Multiple imputation Nonignorable missing data |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These two authors contributed equally to the work. |
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| SubjectTerms | Bayesian analysis Bayesian vector autoregressive model Dependent variables Intensive longitudinal data Missing data Multiple imputation Nonignorable missing data Time series |
| Title | A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes |
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