Premium
A C‐index for recurrent event data: Application to hospitalizations among dialysis patients
Author(s) -
Kim Sehee,
Schaubel Douglas E.,
McCullough Keith P.
Publication year - 2018
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12761
Subject(s) - dialysis , index (typography) , event (particle physics) , event data , medicine , statistics , intensive care medicine , emergency medicine , computer science , mathematics , physics , quantum mechanics , covariate , world wide web
Summary We propose a C‐index (index of concordance) applicable to recurrent event data. The present work addresses the dearth of measures for quantifying a regression model's ability to discriminate with respect to recurrent event risk. The data which motivated the methods arise from the Dialysis Outcomes and Practice Patterns Study (DOPPS), a long‐running prospective international study of end‐stage renal disease patients on hemodialysis. We derive the theoretical properties of the measure under the proportional rates model (Lin et al., 2000), and propose computationally convenient inference procedures based on perturbed influence functions. The methods are shown through simulations to perform well in moderate samples. Analysis of hospitalizations among a cohort of DOPPS patients reveals substantial improvement in discrimination upon adding country indicators to a model already containing basic clinical and demographic covariates, and further improvement upon adding a relatively large set of comorbidity indicators.