Modeling Multiple Time-Varying Related Groups: A Dynamic Hierarchical Bayesian Approach With an Application to the Health and Retirement Study

Kiranmoy Das, Pulak Ghosh and Michael J. Daniels

Journal of the American Statistical Association, 2021, vol. 116, issue 534, 558-568

Abstract: As the population of the older individuals continues to grow, it is important to study the relationship among the variables measuring financial health and physical health of the older individuals to better understand the demand for healthcare, and health insurance. We propose a semiparametric approach to jointly model these variables. We use data from the Health and Retirement Study which includes a set of correlated longitudinal variables measuring financial and physical health. In particular, we propose a dynamic hierarchical matrix stick-breaking process prior for some of the model parameters to account for the time dependent aspects of our data. This prior introduces dependence among the parameters across different groups which varies over time. A Lasso type shrinkage prior is specified for the covariates with time-invariant effects for selecting the set of covariates with significant effects on the outcomes. Through joint modeling, we are able to study the physical health of the older individuals conditional on their financial health, and vice-versa. Based on our analysis, we find that the health insurance (medicare) provided by the government (of the United States) to the older individuals is very effective, and it covers most of the medical expenditures. However, none of the health insurances conveniently cover the additional medical expenses due to chronic diseases like cancer and heart problem. Simulation studies are performed to assess the operating characteristics of our proposed modeling approach. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2021.1886105 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:116:y:2021:i:534:p:558-568

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2021.1886105

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().