Evaluating Dynamic and Predictive Discrimination for Recurrent Event Models: Use of a Time-Dependent C-index

Authors

Jian Wang
Xinyang Jiang
Jing Ning

Publication Date

10-1-2024

Journal

Biostatistics

Abstract

Interest in analyzing recurrent event data has increased over the past few decades. One essential aspect of a risk prediction model for recurrent event data is to accurately distinguish individuals with different risks of developing a recurrent event. Although the concordance index (C-index) effectively evaluates the overall discriminative ability of a regression model for recurrent event data, a local measure is also desirable to capture dynamic performance of the regression model over time. Therefore, in this study, we propose a time-dependent C-index measure for inferring the model's discriminative ability locally. We formulated the C-index as a function of time using a flexible parametric model and constructed a concordance-based likelihood for estimation and inference. We adapted a perturbation-resampling procedure for variance estimation. Extensive simulations were conducted to investigate the proposed time-dependent C-index's finite-sample performance and estimation procedure. We applied the time-dependent C-index to three regression models of a study of re-hospitalization in patients with colorectal cancer to evaluate the models' discriminative capability.

Keywords

Humans, Models, Statistical, Patient Readmission, Colorectal Neoplasms, Time Factors, Recurrence, Likelihood Functions, Regression Analysis, Computer Simulation

Comments

This article has been corrected. See Biostatistics. 2024 Aug 26;26(1):kxae029.

DOI

10.1093/biostatistics/kxad031

PMID

37952117

PMCID

PMC11471962

PubMedCentral® Posted Date

11-10-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes