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Related Experiment Video

Updated: Jan 10, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Simulation-Based Bayesian Predictive Probability of Success for Interim Monitoring of Clinical Trials With Competing

Chiara Micoli1, Alessio Crippa1, Jason T Connor2,3

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Pharmaceutical Statistics
|November 25, 2025
PubMed
Summary

This study introduces a novel simulation-based method to calculate Bayesian predictive probabilities of success (PPoS) for clinical trials with competing risks. This approach enables better trial optimization and futility stopping decisions.

Keywords:
Bayesian predictive probability of successclinical trialscompeting eventsinterim analysis

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Epidemiology

Background:

  • Bayesian predictive probabilities of success (PPoS) are crucial for optimizing clinical trial design and decision-making.
  • Existing methods for PPoS calculation do not adequately address clinical trials with competing event data.

Purpose of the Study:

  • To develop and describe a simulation-based approach for computing PPoS in clinical trials involving competing events.
  • To provide a methodology for utilizing interim data to predict trial success probabilities.

Main Methods:

  • Modeling the joint distribution of time-to-event and event type using Bayesian models for cause-specific hazards.
  • Employing a simulation-based procedure to predict trial outcomes and calculate PPoS.
  • Numerically averaging the probability of success over the posterior distribution of model parameters.

Main Results:

  • The proposed method allows for the computation of PPoS in the presence of competing risks.
  • Demonstrated the application of the method using data from COVID-19 and prostate cancer trials.
  • Showcased how prior distribution choices influence PPoS assessment under various scenarios.

Conclusions:

  • The developed simulation-based approach provides a viable method for PPoS calculation in competing risks clinical trials.
  • This methodology can aid in optimizing trial size and making informed decisions regarding futility.
  • The study highlights the importance of accounting for competing events in Bayesian trial monitoring.