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An R-Based Landscape Validation of a Competing Risk Model
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Adaptive designs for the one-sample log-rank test.

Rene Schmidt1, Andreas Faldum1, Robert Kwiecien1

  • 1Institute of Biostatistics and Clinical Research, University of Muenster, 48149 Muenster, Germany.

Biometrics
|September 24, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive one-sample log-rank test for phase IIa cancer trials using time-to-event data. The novel method improves trial power and reduces sample size compared to fixed-design trials.

Keywords:
Adaptive designPhase IIa trialReference survival curveSingle-arm designSurvival analysis

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Phase IIa cancer trials traditionally use single-arm designs with binary outcomes.
  • Time-to-event endpoints are more suitable for trials with loss to follow-up.
  • The one-sample log-rank test compares patient survival to a reference curve.

Purpose of the Study:

  • To develop and validate an adaptive one-sample log-rank test for phase IIa cancer trials.
  • To incorporate data-dependent sample size reassessment.
  • To improve trial efficiency and power.

Main Methods:

  • Proving convergence of the one-sample log-rank statistic to Brownian motion using martingale central limit theorem.
  • Developing an adaptive test using the inverse normal method with two distinct strategies.
  • Accounting for staggered patient entry times.

Main Results:

  • The proposed adaptive test can rescue underpowered trials.
  • The adaptive test lowers the average sample number (ASN) under the null hypothesis compared to fixed designs.
  • Simulations demonstrate the effectiveness of the proposed methods.

Conclusions:

  • The adaptive one-sample log-rank test offers a flexible and efficient approach for phase IIa cancer trials with time-to-event endpoints.
  • This method enhances statistical power and optimizes resource allocation.
  • The findings support the adoption of adaptive designs in oncology research.