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Sequential rerandomization.

Quan Zhou1, Philip A Ernst1, Kari Lock Morgan2

  • 1Department of Statistics, Rice University, 6100 Main St., Houston, Texas 77005, U.S.A.

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|September 4, 2018
PubMed
Summary
This summary is machine-generated.

Sequential rerandomization, enrolling experimental units in groups, offers superior covariate balance compared to traditional one-time rerandomization. This adaptive method is crucial for practical experiments where units enroll over time.

Keywords:
Experimental designMahalanobis distanceNoncentral chi-squared distributionSequential enrolment

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Area of Science:

  • Statistics
  • Experimental Design
  • Clinical Trials

Background:

  • Traditional rerandomization assigns all units at once.
  • Sequential unit enrollment is common in practice, especially for rare diseases.
  • Existing methods lack a framework for sequential rerandomization.

Purpose of the Study:

  • To develop a mathematical framework for sequential rerandomization designs.
  • To formulate an adaptive procedure for balancing treatment/control assignments.
  • To compare the covariate balance of sequential versus one-time rerandomization.

Main Methods:

  • Developed a mathematical framework for sequential rerandomization.
  • Formulated an adaptive procedure using Mahalanobis distance for imbalance.
  • Analyzed expected covariate balance under sequential enrollment in groups.

Main Results:

  • Sequential rerandomization provides a framework for grouped unit enrollment.
  • The adaptive procedure balances treatment/control assignments using Mahalanobis distance.
  • Sequential rerandomization achieves better expected covariate balance than one-time rerandomization with equal rerandomizations.

Conclusions:

  • Sequential rerandomization is a viable and effective alternative to one-time rerandomization.
  • This method improves covariate balance in practical experimental settings with sequential enrollment.
  • The proposed framework supports adaptive balancing for continuous or binary covariates.