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Simulation-Based Power Analysis for Time-Dependent Area Under Receiver Operating Characteristic Curve Using
1Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, Florida, USA.
Statistics in Medicine
|June 10, 2026
Summary
This study introduces a simulation framework to analyze biomarker prognostic accuracy for time-to-event outcomes. It accurately estimates sample size, power, and effect size for clinical trial designs.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Biomarker Research
Background:
- Assessing biomarker prognostic accuracy for time-to-event outcomes is crucial in clinical research.
- Existing methods may not fully address complexities in study designs and biomarker types.
Purpose of the Study:
- To propose a novel simulation-based power analysis framework for prognostic biomarker studies.
- To enable accurate estimation of sample size, biomarker effect size, and statistical power.
Main Methods:
- Developed a framework integrating Approximate Bayesian Computation (ABC) for pseudo-censored survival data generation.
- Employed iterative Monte Carlo simulations for power and sample size estimation.
- Accommodated single/staggered entry and continuous/dichotomized biomarkers.
Main Results:
- Simulation studies confirmed the framework's accuracy and consistency across various designs.
- Demonstrated reliable estimation of key statistical parameters.
- Validated the approach with a real-world clinical trial in lymphoma.
Conclusions:
- The proposed simulation framework offers a robust tool for designing prognostic biomarker studies.
- It enhances the ability to plan and power clinical trials for time-to-event outcomes.
- Facilitates more efficient and reliable biomarker evaluation in oncology and beyond.
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