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Propensity score-incorporated adaptive design approaches when incorporating real-world data.

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Summary

This study extends the propensity score-integrated composite likelihood (PSCL) method for adaptive clinical trial designs. It enables early success claims or sample size re-estimation using real-world data (RWD).

Keywords:
Cui-Hung-Wang testFisher's combination testPSCLRWDRWEadaptive designcomposite likelihoodoutcome-free designpropensity scorereal-world datareal-world evidence

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Real-World Data Analysis

Background:

  • Real-world data (RWD) can augment prospective clinical studies.
  • The propensity score-integrated composite likelihood (PSCL) method integrates RWD by forming strata based on propensity scores (PS) and down-weighting RWD information.
  • PSCL was initially developed for fixed study designs.

Purpose of the Study:

  • To extend the PSCL method to an adaptive design framework.
  • To enable early success claims or sample size re-estimation in clinical trials using RWD.
  • To propose a general strategy for implementing these adaptive features within PSCL.

Main Methods:

  • Extension of the PSCL method to an adaptive design.
  • Utilization of Fisher's combination test for early success claims.
  • Application of the Cui, Hung, and Wang test for sample size re-estimation.

Main Results:

  • A general strategy for adaptive PSCL is proposed.
  • Demonstration of early success claim procedures.
  • Demonstration of sample size re-estimation procedures.

Conclusions:

  • The proposed adaptive PSCL strategy effectively integrates RWD for enhanced clinical trial design.
  • The methods allow for flexibility in trial conduct, including early stopping or sample size adjustments.
  • The approach provides a robust framework for leveraging RWD in adaptive clinical trials.