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Complex survival trial design by the product integration method.

Yongqiang Tang1

  • 1Department of Biometrics, Grifols, Research Triangle Park, North Carolina.

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|December 15, 2021
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Summary
This summary is machine-generated.

Nonproportional hazards (NPHs) in cancer trials reduce log-rank test efficiency. A new method using multistate Markov models accurately calculates sample sizes for NPH, improving survival trial design.

Keywords:
delayed effectmultistate Markov modelnonproportional hazardstreatment switching

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Clinical Trial Design

Background:

  • Nonproportional hazards (NPHs) are common in cancer immunotherapy trials, complicating traditional survival analysis.
  • The classical log-rank test is inefficient under NPH, and Cox model hazard ratios are difficult to interpret.
  • Weighted log-rank tests and comparisons of restricted mean survival time (RMST) or milestone survival are gaining traction for NPH.

Purpose of the Study:

  • To develop a sample size determination method for survival trials exhibiting nonproportional hazards (NPH).
  • To address challenges in designing multistate models for complex NPH patterns, including delayed effects, heterogeneity, and treatment switching.
  • To evaluate the accuracy and performance of the proposed sample size calculation method.

Main Methods:

  • A sample size determination method based on product integration within a continuous-time multistate Markov model framework.
  • Modeling complex NPH patterns, including delayed treatment effects, individual heterogeneity in lag duration, cure fractions, and treatment switching.
  • Numerical integration for sample size calculations for various NPH scenarios.

Main Results:

  • The power of milestone survival and RMST tests is influenced by trial duration and milestone timepoint, not always increasing with the latter.
  • RMST tests can outperform the log-rank test with appropriate milestone timepoints, especially in diminishing treatment effect or proportional hazards cure models.
  • Generally, RMST tests show lower power compared to Fleming-Harrington weighted log-rank tests.

Conclusions:

  • The proposed product integration method provides an accurate approach for sample size determination in survival trials with NPH.
  • Understanding the interplay between trial duration, milestone timepoints, and test power is crucial for optimal trial design.
  • The choice of statistical test (RMST vs. weighted log-rank) depends on the specific NPH characteristics and desired power.