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Two-sample survival tests based on control arm summary statistics.

Jannik Feld1, Moritz Fabian Danzer1, Andreas Faldum1

  • 1Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany.

Plos One
|June 14, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel survival test for single-arm trials. The new method accurately compares patient survival against historical data, even when only a survival curve is available, avoiding inflated error rates.

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Area of Science:

  • Biostatistics
  • Clinical Trial Analysis
  • Survival Analysis

Background:

  • The one-sample log-rank test is standard for single-arm survival trials, comparing patient outcomes to a reference curve.
  • Classical tests assume the reference curve is known, ignoring potential sampling errors from historical data.
  • Ignoring reference curve variability can inflate the type I error rate in survival analyses.

Purpose of the Study:

  • To develop a new survival test that accounts for the sampling error of estimated historical reference curves.
  • To enable valid historical comparisons in single-arm trials when only a survival curve, not full historical data, is available.
  • To provide a method applicable when the two-sample log-rank test is not feasible due to data limitations.

Main Methods:

  • Proposed a novel survival test addressing the sampling error of reference survival curves.
  • Developed sample size calculation formulas for the new test.
  • Conducted a simulation study to evaluate the new test's performance.

Main Results:

  • The new test effectively accounts for the sampling error of the reference curve.
  • Demonstrated the test's validity even when only a historical survival curve is available.
  • Simulation results showed the proposed method controls type I error rates.

Conclusions:

  • The new survival test offers a valid approach for historical comparisons in single-arm trials.
  • This method is crucial when individual historical patient data is unavailable.
  • The developed formulas and validated test enhance the reliability of survival trial outcomes.