Non-parametric recurrent events analysis with BART and an application to the hospital admissions of patients with diabetes. Biostatistics 2020 Jan 01;21(1):69-85
Date
07/31/2018Pubmed ID
30059992Pubmed Central ID
PMC6920553DOI
10.1093/biostatistics/kxy032Scopus ID
2-s2.0-85076993832 (requires institutional sign-in at Scopus site) 7 CitationsAbstract
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 JRAuthors
John R. Meurer MD, MBA Institute Director, Professor in the Institute for Health and Equity department at Medical College of WisconsinLisa E. Rein Biostatistician III 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
AdultAged
Aged, 80 and over
Biostatistics
Computer Simulation
Diabetes Mellitus
Female
Humans
Male
Middle Aged
Models, Statistical
Patient Admission
Young Adult
