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Personalized Risk-Based Screening Design for Comparative Two-Arm Group Sequential Clinical Trials.

Yeonhee Park1

  • 1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53705, USA.

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|March 25, 2022
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This study introduces a personalized risk-based screening design for adaptive randomization in clinical trials. It improves treatment allocation and reduces patient failures in personalized medicine settings.

Keywords:
Bayesian inferenceadaptive randomizationclinical trialspersonalized medicineprobit modelscreening

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Personalized Medicine

Background:

  • Personalized medicine requires incorporating patient variability for improved clinical benefits.
  • Adaptive randomization aims to optimize treatment allocation based on patient characteristics.
  • Existing adaptive randomization methods face challenges with selection bias and prognostic covariates.

Purpose of the Study:

  • To propose a novel personalized risk-based screening design for adaptive randomization.
  • To address challenges in controlling selection bias and prognostic covariates in adaptive randomization.
  • To improve treatment allocation and clinical outcomes in personalized medicine clinical trials.

Main Methods:

  • Utilized Bayesian covariate-adjusted response-adaptive randomization.
  • Developed personalized risk-based allocation probabilities for adaptive randomization.
  • Calibrated Bayesian adaptive decision rules to maintain statistical error rates.

Main Results:

  • The proposed design effectively controls error rates in simulations.
  • Demonstrated a significant reduction in patient failures compared to existing designs.
  • Showed a higher proportion of patients allocated to more beneficial interventions.

Conclusions:

  • The personalized risk-based screening design is effective for randomized controlled clinical trials in personalized medicine.
  • This approach enhances patient outcomes by optimizing treatment allocation.
  • The design offers a robust solution for bias control in adaptive randomization.