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Updated: Apr 25, 2026

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Estimation, testing and sample size calculation within the responder-stratified exponential survival model.

Samuel Kilian1, Marietta Kirchner1, Meinhard Kieser1

  • 1Institute of Medical Biometry, Heidelberg University, Heidelberg, Germany.

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This study introduces new methods for phase III oncology trials using a responder stratified exponential survival (RSES) model. The novel approximate test shows higher power when survival benefits stem from increased responders.

Keywords:
Accelerated approvalstratificationsurvival

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Oncology

Background:

  • Phase III oncology trials often use overall survival as a primary endpoint, leading to long observation periods.
  • Preliminary approval based on surrogate endpoints is crucial to avoid withholding promising therapies.

Purpose of the Study:

  • To develop novel statistical methods for analyzing phase III oncology trials using a surrogate endpoint.
  • To introduce new estimators, an approximate test, and a sample size calculation method within the responder stratified exponential survival (RSES) model.

Main Methods:

  • Development of novel estimators and an approximate test for the RSES model.
  • Sample size calculation method tailored for the RSES model.
  • Comparison of the approximate test against logrank and stratified logrank tests.

Main Results:

  • The developed methods perform well under RSES model assumptions.
  • The approximate test demonstrates superior power when survival benefits are driven by a higher proportion of responders.
  • Type I error rate of the approximate test may exceed 5% for sample sizes below 100.

Conclusions:

  • The RSES model and associated methods offer a valuable framework for phase III oncology trials with binary surrogate endpoints.
  • The approximate test provides increased power in specific scenarios but requires careful consideration of its Type I error rate and model assumptions.