Work

Quantifying and estimating the predictive accuracy for censored time‐to‐event data with competing risks

Year: 2018

Type: article

Abstract: This paper focuses on quantifying and estimating the predictive accuracy of prognostic models for time-to-event outcomes with competing events. We consider the time-dependent discrimination and calibration metrics, including the receiver operating characteristics curve and the Brier score, in the context of competing risks. To address censoring, we propose a unified nonparametric estimation framework for both discrimination and calibration measures, by weighting the censored subjects with the conditional probability of the event of interest given the observed data. The proposed method can be extended to time-dependent predictive accuracy metrics constructed from a general class of loss functions. We apply the methodology to a data set from the African American Study of Kidney Disease and Hypertension to evaluate the predictive accuracy of a prognostic risk score in predicting end-stage renal disease, accounting for the competing risk of pre-end-stage renal disease death, and evaluate its numerical performance in extensive simulation studies.

Source: Statistics in Medicine

DOI: 10.1002/sim.7806

Authors: Cai Wu, Liang Li

Institutions: The University of Texas MD Anderson Cancer Center, The University of Texas Health Science Center at Houston

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, University of Texas MD Anderson Cancer Center, National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases