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Semiparametric Survival Analysis of 30-Day Hospital Readmissions with Bayesian Additive Regression Kernel Model

Author

Listed:
  • Sounak Chakraborty

    (Department of Statistics, University of Missouri, Columbia, MO 65211, USA)

  • Peng Zhao

    (Liberty Mutual Group Inc., Boston, MA 02116, USA)

  • Yilun Huang

    (Department of Statistics, University of Missouri, Columbia, MO 65211, USA)

  • Tanujit Dey

    (Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02120, USA)

Abstract

In this paper, we introduce a kernel-based nonlinear Bayesian model for a right-censored survival outcome data set. Our kernel-based approach provides a flexible nonparametric modeling framework to explore nonlinear relationships between predictors with right-censored survival outcome data. Our proposed kernel-based model is shown to provide excellent predictive performance via several simulation studies and real-life examples. Unplanned hospital readmissions greatly impair patients’ quality of life and have imposed a significant economic burden on American society. In this paper, we focus our application on predicting 30-day readmissions of patients. Our survival Bayesian additive regression kernel model (survival BARK or sBARK) improves the timeliness of readmission preventive intervention through a data-driven approach.

Suggested Citation

  • Sounak Chakraborty & Peng Zhao & Yilun Huang & Tanujit Dey, 2022. "Semiparametric Survival Analysis of 30-Day Hospital Readmissions with Bayesian Additive Regression Kernel Model," Stats, MDPI, vol. 5(3), pages 1-14, July.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:3:p:38-630:d:863048
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    References listed on IDEAS

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    1. Chakraborty, Sounak & Ghosh, Malay & Mallick, Bani K., 2012. "Bayesian nonlinear regression for large p small n problems," Journal of Multivariate Analysis, Elsevier, vol. 108(C), pages 28-40.
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