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Pair-switching rerandomization.

Ke Zhu1, Hanzhong Liu1

  • 1Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China.

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|June 27, 2022
PubMed
Summary
This summary is machine-generated.

Pair-switching rerandomization (PSRR) offers an efficient method for drawing balanced treatment assignments in clinical trials. This novel approach significantly speeds up randomization processes while maintaining statistical validity for analysis.

Keywords:
Metropolis-Hastings algorithmcausal inferenceclinical trialsexperimental designrandomization-based inferencesequential experiment

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Area of Science:

  • Statistics
  • Clinical Trials
  • Experimental Design

Background:

  • Rerandomization improves estimation and inference efficiency by discarding unbalanced covariate assignments.
  • Traditional rerandomization using acceptance-rejection sampling is computationally inefficient and time-consuming.
  • This inefficiency hinders the generation of assignments needed for randomization tests and confidence intervals.

Purpose of the Study:

  • To propose an efficient method for drawing balanced assignments in rerandomization.
  • To validate the statistical properties and computational speed of the new method.
  • To provide a faster approach for constructing confidence intervals via randomization tests.

Main Methods:

  • Introduced a novel pair-switching rerandomization (PSRR) method for efficient generation of balanced assignments.
  • Demonstrated the unbiasedness and variance reduction of the difference-in-means estimator under PSRR.
  • Developed an exact inversion approach for Fisher randomization tests to confidence intervals.

Main Results:

  • PSRR ensures the validity of Fisher randomization tests.
  • The proposed confidence interval inversion method is faster than existing techniques.
  • Simulation studies show PSRR is 3-23 times faster than classical rerandomization with comparable statistical power.
  • PSRR was successfully applied to two clinical trial datasets, demonstrating its practical advantages.

Conclusions:

  • Pair-switching rerandomization (PSRR) provides a computationally efficient and statistically valid alternative to classical rerandomization.
  • The PSRR method accelerates the generation of balanced assignments, benefiting randomization tests and confidence interval construction.
  • This approach is applicable to both sequential and non-sequential randomized experiments, enhancing its utility in diverse clinical trial settings.