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

Cai Wu1,2, Liang Li2

  • 1Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Statistics in Medicine
|May 17, 2018
PubMed
Summary

This study introduces a new method to accurately assess prognostic models in the presence of competing risks. The framework enhances the evaluation of time-to-event predictions, crucial for clinical decision-making.

Keywords:
Brier scorecompeting risksdiagnostic medicinepredictive accuracytime-dependent ROC

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Research Methodology

Background:

  • Prognostic models are essential for predicting time-to-event outcomes in clinical settings.
  • Competing risks, where multiple events can occur, complicate the accurate estimation of predictive accuracy.
  • Existing methods may not adequately address censoring and time-dependent metrics in competing risks scenarios.

Purpose of the Study:

  • To develop and validate a unified nonparametric framework for estimating time-dependent discrimination and calibration metrics for time-to-event outcomes with competing risks.
  • To propose a method for handling censored data by weighting subjects based on the conditional probability of the event of interest.
  • To evaluate the proposed methodology's performance using simulation studies and a real-world dataset.

Main Methods:

  • Development of a unified nonparametric estimation framework for discrimination and calibration measures.
  • Incorporation of inverse probability of censoring weighting (IPCW) using conditional probabilities for censored subjects.
  • Extension of the framework to accommodate time-dependent predictive accuracy metrics derived from general loss functions.
  • Application to the African American Study of Kidney Disease and Hypertension dataset.

Main Results:

  • The proposed unified framework effectively quantifies and estimates predictive accuracy for time-to-event outcomes with competing risks.
  • The method demonstrates robust performance in simulation studies, handling censoring and time-dependent metrics appropriately.
  • Evaluation of a prognostic risk score for end-stage renal disease prediction in the African American Study of Kidney Disease and Hypertension dataset, accounting for competing mortality.

Conclusions:

  • The developed nonparametric framework provides a reliable approach for assessing prognostic model accuracy in complex competing risks settings.
  • This methodology improves the estimation of time-dependent discrimination and calibration, enhancing the utility of prognostic models.
  • The findings offer valuable tools for researchers and clinicians needing to evaluate predictive models in the presence of competing events.