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Preservation of Type I Error for Partially-Unblinded Sample Size Re-Estimation.

Ann Marie K Weideman1,2, Kevin J Anstrom1,2, Gary G Koch1

  • 1Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

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|March 14, 2025
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Summary
This summary is machine-generated.

This study introduces a novel partially-unblinded method for sample size re-estimation (SSR) in clinical trials. This approach maintains the Type I error rate while allowing for adjustments based on interim data for both binary and continuous outcomes.

Keywords:
adaptive designinterim analysissample size adjustmenttype I error preservationunequal treatment allocationvariance heterogeneity

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methodology

Background:

  • Sample size re-estimation (SSR) is crucial for adaptive clinical trials, enabling adjustments based on accrued data.
  • Current SSR methods use blinded or unblinded approaches, aiming to preserve the Type I error rate.
  • A need exists for SSR methods that balance operational feasibility with statistical rigor.

Purpose of the Study:

  • To propose and evaluate a partially-unblinded method for sample size re-estimation (SSR) in clinical trials.
  • To assess the impact of this method on Type I error rates for binary and continuous endpoints.
  • To explore and clarify mathematical expressions for SSR under various variance scenarios, including dual variance.

Main Methods:

  • Developed a partially-unblinded SSR approach, using interim data without the effect size to maintain operational blinding.
  • Conducted proof-of-concept and simulation studies to validate the method's performance.
  • Investigated SSR mathematical expressions for homogeneity, heterogeneity, and dual variance scenarios for binary and continuous data.

Main Results:

  • The proposed partially-unblinded SSR method effectively preserves the Type I error rate.
  • Demonstrated the method's applicability to both binary and continuous endpoints.
  • Derived and clarified dual variance mathematical expressions for SSR, showing they offer a compromise between homogeneity and heterogeneity, bounded sample size estimates, and suitability for adaptive designs.

Conclusions:

  • Partially-unblinded SSR offers a viable strategy for adaptive trial design, maintaining statistical integrity.
  • The developed method provides flexibility in sample size adjustments without compromising the Type I error rate.
  • The findings extend the utility of SSR methods, particularly the dual variance approach, to a broader range of clinical trial designs.