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Sample Size Calculation Under Nonproportional Hazards Using Average Hazard Ratios
Ina Dormuth1, Markus Pauly1,2, Geraldine Rauch3,4
1Department of Statistics, TU Dortmund University, Dortmund, Germany.
Biometrical Journal. Biometrische Zeitschrift
|August 12, 2024
Summary
The average hazard ratio (AHR) offers a powerful alternative to traditional hazard ratios for clinical trials with nonproportional hazards. Simulation-based sample size calculations for AHR tests enhance statistical power and improve sample efficiency.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Survival Analysis
Background:
- Time-to-event endpoints are crucial in clinical trials.
- Hazard ratios (HRs) are commonly used but assume proportional hazards (PHs).
- Nonproportional hazards (N-PHs) limit the interpretability and applicability of standard HRs.
Purpose of the Study:
- To introduce and facilitate the practical application of the average hazard ratio (AHR) as an effect measure.
- To develop and assess methods for sample size calculation for AHR tests.
- To address the limitations of HRs in scenarios with nonproportional hazards.
Main Methods:
- Developed sample size calculation approaches for AHR tests.
- Conducted extensive simulation studies to evaluate sample size calculation reliability.
- Simulations covered diverse survival and censoring distributions, including both proportional and nonproportional hazards.
Main Results:
- The average hazard ratio (AHR) effectively handles time-varying effects without requiring proportional hazards.
- Simulation-based sample size calculation approaches are reliable for designing clinical trials with N-PHs.
- Utilizing AHR can lead to increased statistical power and more efficient sample sizes compared to traditional methods.
Conclusions:
- The average hazard ratio (AHR) is a valuable tool for analyzing time-to-event data, particularly when hazards are nonproportional.
- Simulation-based sample size calculations enhance the design of clinical trials employing AHR.
- AHR facilitates more powerful and sample-efficient detection of group differences in time-to-event outcomes.
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