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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Inference Under Covariate-Adaptive Randomization Using Random Center-Effect.

Anjali Pandey1, Harsha Shree Bs1, Andrea Callegaro2

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Biometrical Journal. Biometrische Zeitschrift
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Summary
This summary is machine-generated.

This study introduces a random-effect model for covariate-adaptive randomization in multicenter trials. The proposed method effectively controls type-I error and maintains statistical power for various endpoints.

Keywords:
covariate‐adaptive randomizationminimizationpowerrandom‐effect modeltype‐I error

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Statistical Modeling

Background:

  • Minimization is popular for covariate-adaptive randomization in multicenter trials.
  • Including minimization variables in analysis controls type-I error.
  • Recruitment center, a minimization variable with many categories, is often excluded from models.

Purpose of the Study:

  • To propose and assess a random-effect model for including the 'center' minimization variable.
  • To evaluate the performance of this model for Gaussian, binary, and Poisson endpoint variables.
  • To offer an alternative to re-randomization tests for sensitivity analysis.

Main Methods:

  • Developed a statistical model incorporating the 'center' variable as a random effect.
  • Conducted simulation studies using Gaussian, binary, and Poisson endpoint variables.
  • Assessed type-I error control and statistical power under various clinical trial settings.

Main Results:

  • The random-effect model effectively controls type-I error across all tested endpoint types.
  • Maximum statistical power is preserved for Gaussian, binary, and Poisson endpoints.
  • The proposed model demonstrates robust performance in varied clinical trial simulations.

Conclusions:

  • Including the 'center' variable as a random effect is a valid approach for covariate-adaptive randomization.
  • This method provides a reliable alternative to re-randomization tests for sensitivity analysis.
  • The random-effect model ensures statistical integrity and power in multicenter trials.