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Confirmatory Adaptive Designs for Clinical Trials With Multiple Time-to-Event Outcomes in Multi-state Markov Models.

Moritz Fabian Danzer1, Andreas Faldum1, Thorsten Simon2

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

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|October 15, 2024
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Summary
This summary is machine-generated.

This study introduces a novel multistate model for analyzing multiple time-to-event outcomes in clinical trials. This approach enables interim analyses and data-dependent design adaptations, particularly for progression-free survival (PFS) and overall survival (OS) in oncology.

Keywords:
clinical triallog‐rank testsample size recalculationsurvival analysis

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Oncology Research

Background:

  • Existing methods for analyzing multiple time-to-event outcomes in clinical trials may not fully utilize available data for interim analyses.
  • Disease characteristics and study planning can necessitate interim analyses and study design adaptations.
  • Endpoint dependencies can limit the use of full information for adaptive trial designs.

Purpose of the Study:

  • To propose a flexible statistical method for analyzing multiple time-to-event outcomes in clinical trials.
  • To enable interim analyses and data-dependent study design adaptations by accounting for endpoint dependencies.
  • To specifically address the simultaneous analysis of progression-free survival (PFS) and overall survival (OS) in oncological trials.

Main Methods:

  • Embedding multiple time-to-event endpoints within a Markovian multistate model.
  • Developing a flexible test procedure applicable to various scenarios.
  • Utilizing simulation studies to evaluate the method's performance with small sample sizes.

Main Results:

  • The proposed multistate model effectively incorporates disease history for patient data.
  • The method allows for data-dependent adaptations in clinical trial designs.
  • Simulation studies confirmed the method's properties for small sample sizes.

Conclusions:

  • Multistate models offer a robust solution for analyzing dependent time-to-event outcomes in clinical trials.
  • This approach facilitates adaptive trial designs, crucial for oncology research involving PFS and OS.
  • The developed test procedure is flexible and demonstrated effective in a real-world oncological study dataset.