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

Sounak Chakraborty, Peng Zhao, Yilun Huang and Tanujit Dey
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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

Stats, 2022, vol. 5, issue 3, 1-14

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.

Keywords: kernel method; Bayesian analysis; survival outcome; right censoring; hospital readmission (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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