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The Impact of Two Data-Generating Processes for Competing Risk Data on the Discrimination and Calibration of Two

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  • 1ICES, Toronto, Ontario, Canada.

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|February 27, 2026
PubMed
Summary

Choosing the right data-generating process for competing risk simulations is crucial. Discordance minimally impacted discrimination metrics but is important for calibration accuracy.

Keywords:
Monte Carlo simulationscompeting risksdata‐generating processmodel calibrationmodel discrimination

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Monte Carlo simulations are vital in statistical research, particularly for survival analysis with competing risks.
  • Competing risks are events that prevent the primary event of interest from occurring.
  • Existing methods for simulating competing risk data rely on either cause-specific hazards or subdistribution hazards.

Purpose of the Study:

  • To investigate the impact of different data-generating processes on prediction model performance in competing risk scenarios.
  • To evaluate how the choice of data-generating process affects discrimination (time-dependent AUC) and accuracy (time-dependent Brier score).
  • To assess the influence on calibration metrics (ICI, E50, E90) based on competing risk regression choices.

Main Methods:

  • Utilized Monte Carlo simulations to generate competing risk data using two distinct processes.
  • Assessed prediction model performance using time-dependent Area Under the Curve (AUC) for discrimination.
  • Evaluated accuracy with the time-dependent Brier score and calibration using Integrated Calibration Index (ICI), E50, and E90.

Main Results:

  • The impact of discordance between the data-generating process and the fitted model was minimal on time-dependent AUC and time-dependent Brier score.
  • Calibration metrics (ICI, E50, E90) were sensitive to the choice of regression model used for smoothed event probabilities.
  • Concordance between the fitted model and the data-generating process is recommended for accurate calibration assessment.

Conclusions:

  • For assessing discrimination in competing risk models, the choice of data-generating process has a limited impact.
  • Accurate calibration assessment requires using a smoothed event probability model that matches the type of model being evaluated.
  • Researchers should carefully consider the data-generating process and model concordance for reliable survival analysis predictions.