Medical College of Wisconsin
CTSICores SearchResearch InformaticsREDCap

Non-parametric recurrent events analysis with BART and an application to the hospital admissions of patients with diabetes. Biostatistics 2020 01 01;21(1):69-85

Date

07/31/2018

Pubmed ID

30059992

Pubmed Central ID

PMC6920553

DOI

10.1093/biostatistics/kxy032

Scopus ID

2-s2.0-85076993832

Abstract

Much of survival analysis is concerned with absorbing events, i.e., subjects can only experience a single event such as mortality. This article is focused on non-absorbing or recurrent events, i.e., subjects are capable of experiencing multiple events. Recurrent events have been studied by many; however, most rely on the restrictive assumptions of linearity and proportionality. We propose a new method for analyzing recurrent events with Bayesian Additive Regression Trees (BART) avoiding such restrictive assumptions. We explore this new method via a motivating example of hospital admissions for diabetes patients and simulated data sets.

Author List

Sparapani RA, Rein LE, Tarima SS, Jackson TA, Meurer JR

Authors

John R. Meurer MD, MBA Institute Director, Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Rodney Sparapani PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Sergey S. Tarima PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Adult
Aged
Aged, 80 and over
Biostatistics
Computer Simulation
Diabetes Mellitus
Female
Humans
Male
Middle Aged
Models, Statistical
Patient Admission
Young Adult
jenkins-FCD Prod-486 e3098984f26de787f5ecab75090d0a28e7f4f7c0