A C-Index for Recurrent Event Data: Application to Hospitalizations among Dialysis Patients

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 Outco...

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Bibliographic Details
Published in:Biometrics 2018-06, Vol.74 (2), p.734-743
Main Authors: Kim, Sehee, Schaubel, Douglas E., McCullough, Keith P.
Format: Article
Language:English
Subjects:
Adult
BIOMETRIC PRACTICE
Comorbidity
Computer Simulation
C‐index
Data Interpretation, Statistical
Data processing
Demographics
Demography - statistics & numerical data
Dialysis
Hemodialysis
Hospitalization - statistics & numerical data
Humans
Indicators
Influence functions
Kidney Failure, Chronic - therapy
Model discrimination
Models, Statistical
Patients
Proportional rates model
Recurrence
Recurrent events
Regression Analysis
Regression models
Renal Dialysis
Wild bootstrap
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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.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.12761