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Inference under Covariate-Adaptive Randomization.

Federico A Bugni1, Ivan A Canay2, Azeem M Shaikh3

  • 1Department of Economics, Duke University, federico.bugni@duke.edu.

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|March 26, 2019
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Summary
This summary is machine-generated.

This study introduces adjusted statistical tests for analyzing average treatment effects in randomized trials with covariate-adaptive randomization. These methods ensure accurate hypothesis testing, improving reliability in clinical research.

Keywords:
Covariate-adaptive randomizationEfron’s biased-coin designpermutation testrandomized controlled trialstrata fixed effectsstratified block randomizationtreatment assignmenttwo-sample t-test

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Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Trials

Background:

  • Covariate-adaptive randomization balances baseline covariates within strata during treatment assignment.
  • Standard statistical tests may yield conservative or overly liberal results under such randomization schemes.
  • Accurate inference for average treatment effects is crucial for reliable clinical trial outcomes.

Purpose of the Study:

  • To develop and validate statistical tests for average treatment effect inference in randomized controlled trials using covariate-adaptive randomization.
  • To address the conservativeness of standard two-sample t-tests and regression-based tests in this setting.
  • To propose adjustments for permutation tests to ensure exactness under various covariate-adaptive randomization schemes.

Main Methods:

  • Theoretical analysis of limiting rejection probabilities for standard and adjusted two-sample t-tests under covariate-adaptive randomization.
  • Investigation of t-tests within linear regression models, considering treatment assignment and stratum indicators.
  • Development and evaluation of a covariate-adaptive permutation test, including adjustments for specific balance criteria.

Main Results:

  • A simple standard error adjustment makes the two-sample t-test exact for average treatment effect hypothesis testing.
  • The standard regression-based t-test is exact only for specific randomization schemes (π=0.5) but can be adjusted for general use.
  • The covariate-adaptive permutation test, with appropriate adjustments, provides exact inference for strong balance randomization schemes.

Conclusions:

  • Adjusted statistical tests, including modified t-tests and permutation tests, offer exact inference for average treatment effects under covariate-adaptive randomization.
  • These findings provide practical recommendations for improving the statistical rigor of randomized controlled trials employing complex randomization methods.
  • The study highlights the importance of accounting for specific randomization designs in hypothesis testing for treatment effects.