Time-Dependent Pseudo $\boldsymbol{R^2}$ for Assessing Predictive Performance in Competing Risks Data

TL;DR

This paper introduces a new time-dependent pseudo R^2 measure for evaluating predictive performance in competing risks time-to-event data, addressing limitations of traditional metrics like the C-index and Brier score.

Contribution

The paper proposes a novel pseudo R^2 measure tailored for competing risks data, with theoretical properties and demonstrated advantages over existing metrics.

Findings

The new measure performs better in simulations than traditional metrics.

It provides a more reliable assessment of predictive accuracy over time.

Real data applications confirm its practical utility.

Abstract

Evaluating and validating the performance of prediction models is a fundamental task in statistics, machine learning, and their diverse applications. However, developing robust performance metrics for competing risks time-to-event data poses unique challenges. We first highlight how certain conventional predictive performance metrics, such as the C-index, Brier score, and time-dependent AUC, can yield undesirable results when comparing predictive performance between different prediction models. To address this research gap, we introduce a novel time-dependent pseudo $R^2$ measure to evaluate the predictive performance of a predictive cumulative incidence function over a restricted time domain under right-censored competing risks time-to-event data. Specifically, we first propose a population-level time-dependent pseudo $R^2$ measures for the competing risk event of interest and then define their corresponding sample versions based on right-censored competing risks time-to-event data. We investigate the asymptotic properties of the proposed measure and demonstrate its advantages over conventional metrics through comprehensive simulation studies and real data applications.