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

• Activity Number: 224 - Recently Developed Survival Analysis Methods with Applications to End-Stage Renal Disease Data
• Type: Topic Contributed
• Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
• Sponsor: ENAR
• Author(s): Sehee Kim* and Douglas Earl Schaubel and Keith P McCullough
• Companies: Universty of Michigan and University of Michigan and Arbor Research Collaborative for Health
• Keywords: C-index ; Model discrimination ; Proportional rates model ; Recurrent events ; Wild bootstrap

Abstract

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.