A Spatial–Temporal Bayesian Model for a Case-Crossover Design with Application to Extreme Heat and Claims Data

Liang, Menglu; Li, Zheng; Zhang, Lijun; Wang, Ming

doi:10.3390/stats7040080

Open AccessArticle

A Spatial–Temporal Bayesian Model for a Case-Crossover Design with Application to Extreme Heat and Claims Data

by

Menglu Liang

^1,*,†,‡

,

Zheng Li

^2,‡,

Lijun Zhang

^3,‡

and

Ming Wang

^3,‡

¹

Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20742, USA

²

Novartis Pharmaceuticals, New Jersey, NJ 07936, USA

³

Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA

^*

Author to whom correspondence should be addressed.

^†

Current address: Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, Baltimore Ave, College Park, MD 20740, USA.

^‡

These authors contributed equally to this work.

Stats 2024, 7(4), 1379-1391; https://doi.org/10.3390/stats7040080

Submission received: 14 October 2024 / Revised: 3 November 2024 / Accepted: 7 November 2024 / Published: 19 November 2024

Download

Browse Figure

Versions Notes

Abstract

Epidemiological approaches for examining human health responses to environmental exposures in observational studies frequently address confounding by employing advanced matching techniques and statistical methods grounded in conditional likelihood. This study incorporates a recently developed Bayesian hierarchical spatiotemporal model within a conditional logistic regression framework to capture the heterogeneous effects of environmental exposures in a case-crossover (CCO) design. Spatial and temporal dependencies are modeled through random effects incorporating multivariate conditional autoregressive priors. Flexible frailty structures are introduced to explore strategies for managing temporal variables. Parameter estimation and inference are conducted using a Monte Carlo Markov chain method within a Bayesian framework. Model fit and optimal model selection are evaluated using the deviance information criterion. Simulations assess and compare model performance across various scenarios. Finally, the approach is illustrated with workers’ compensation claims data from New York and Florida to examine spatiotemporal heterogeneity in hospitalization rates related to heat prostration.

Keywords: case-crossover (CCO) design; environmental epidemiology; Bayesian inference; multivariate conditional autoregressive priors; claims data

Share and Cite

MDPI and ACS Style

Liang, M.; Li, Z.; Zhang, L.; Wang, M. A Spatial–Temporal Bayesian Model for a Case-Crossover Design with Application to Extreme Heat and Claims Data. Stats 2024, 7, 1379-1391. https://doi.org/10.3390/stats7040080

AMA Style

Liang M, Li Z, Zhang L, Wang M. A Spatial–Temporal Bayesian Model for a Case-Crossover Design with Application to Extreme Heat and Claims Data. Stats. 2024; 7(4):1379-1391. https://doi.org/10.3390/stats7040080

Chicago/Turabian Style

Liang, Menglu, Zheng Li, Lijun Zhang, and Ming Wang. 2024. "A Spatial–Temporal Bayesian Model for a Case-Crossover Design with Application to Extreme Heat and Claims Data" Stats 7, no. 4: 1379-1391. https://doi.org/10.3390/stats7040080

APA Style

Liang, M., Li, Z., Zhang, L., & Wang, M. (2024). A Spatial–Temporal Bayesian Model for a Case-Crossover Design with Application to Extreme Heat and Claims Data. Stats, 7(4), 1379-1391. https://doi.org/10.3390/stats7040080

Article Menu

A Spatial–Temporal Bayesian Model for a Case-Crossover Design with Application to Extreme Heat and Claims Data

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI