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Bayesian Spatial Modeling of Incomplete Data with Application to HIV Prevalence in Ghana

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  • Prince Allotey

    (University of Connecticut)

  • Ofer Harel

    (University of Connecticut)

Abstract

Incomplete data are unavoidable and pose serious complications in statistical analyses of epidemiological research. However, many researchers still ignore these complications and proceed with complete case analysis. In this paper, we propose modern procedures to handle incomplete spatially correlated data. We illustrate these procedures with an application to spatially-referenced HIV data from Ghana. The main goal was to identify significant risk factors of HIV prevalence in Ghana via a Bayesian spatial hierarchical modeling approach. Results revealed substantial differences in parameter estimates and highest posterior density intervals widths obtained using our approach and that of complete case analysis. Based on these findings, our approach provides more accurate parameter estimates, reduces the chances of committing a type I error, and has higher predictive efficiency. In addition, results from these findings identified higher-risk regions that require targeted interventions by government to help reduce the spread of HIV in Ghana.

Suggested Citation

  • Prince Allotey & Ofer Harel, 2023. "Bayesian Spatial Modeling of Incomplete Data with Application to HIV Prevalence in Ghana," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(2), pages 307-329, November.
    • Handle: RePEc:spr:sankhb:v:85:y:2023:i:2:d:10.1007_s13571-023-00308-6
    DOI: 10.1007/s13571-023-00308-6
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