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Multiple testing of treatment-effect-modifying biomarkers in a randomized clinical trial with a survival endpoint

Stefan Michiels1, Richard F Potthoff, Stephen L George

  • 1Unit of Biostatistics and Epidemiology, Institut Gustave Roussy, Villejuif, France. stefan.michiels@bordet.be

Statistics in Medicine
|February 24, 2011
PubMed

Insights

This study introduces novel permutation tests to analyze phase III clinical trials with multiple biomarkers and survival endpoints. These methods help identify patient subgroups benefiting from therapies, even with limited pre-trial biomarker information.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Genomics

Background:

  • Genomic advancements and targeted therapies highlight the need for biomarker-defined patient subgroups.
  • Biomarker identification is crucial for both targeted and non-targeted therapies, but tests are often unavailable at pivotal trial stages.
  • Analyzing treatment effects across multiple biomarkers in phase III trials with survival endpoints presents statistical challenges.

Purpose of the Study:

  • To provide guidance for analyzing phase III clinical trials with survival endpoints to identify biomarker-defined treatment effects.
  • To develop statistical methods for evaluating therapy effectiveness in biomarker-positive versus biomarker-negative subgroups when multiple biomarkers are considered.
  • To control the family-wise error rate when assessing treatment-by-biomarker interactions in large patient populations.

Main Methods:

  • Utilized a Weibull regression model to study treatment-by-biomarker interactions.
  • Developed permutation procedures using single and composite biomarker statistics.
  • Accounted for biomarker dependence structures to control the family-wise error rate.
  • Conducted a simulation study to compare the performance of permutation tests under various scenarios.

Main Results:

  • The proposed permutation tests effectively control the family-wise error rate in the presence of multiple, potentially dependent biomarkers.
  • Simulations demonstrated the operational characteristics of the permutation tests across different scenarios.
  • The methods were successfully applied to a phase III adjuvant chemotherapy trial in early breast cancer involving 10 biomarkers and 798 patients.

Conclusions:

  • The developed permutation tests offer a robust framework for analyzing biomarker-defined treatment effects in phase III clinical trials with survival endpoints.
  • These methods are applicable to retrospective biomarker studies and prospective trials where initial knowledge of targeting pathways is limited.
  • This approach enhances the ability to identify patient subgroups that benefit from specific therapies, optimizing treatment strategies.

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