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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

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.
Simple randomization
Simple...
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Wilcoxon Rank-Sum Test01:21

Wilcoxon Rank-Sum Test

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...

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Related Experiment Video

Updated: May 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Weighted re-randomization tests for minimization with unbalanced allocation.

Baoguang Han1, Menggang Yu, Damian McEntegart

  • 1Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN 46285, USA. han_baoguang@lilly.com

Pharmaceutical Statistics
|June 14, 2013
PubMed
Summary
This summary is machine-generated.

A new weighted re-randomization test corrects bias in clinical trials using minimization with unequal allocation. This robust method maintains statistical power, even with temporal trends.

Keywords:
covariate-adaptive randomizationminimizationre-randomization testtemporal trend

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: May 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Biostatistics
  • Clinical Trial Methodology

Background:

  • Re-randomization tests offer a robust alternative to traditional methods for analyzing randomized clinical trials.
  • Minimization is a widely used covariate-adaptive randomization technique for balancing prognostic factors.
  • Fixed-entry-order re-randomization is effective for trials with suspected temporal trends.

Purpose of the Study:

  • To address the bias and power compromise of fixed-entry-order re-randomization tests in trials with minimization and unequal allocation.
  • To propose a novel weighted fixed-entry-order re-randomization test to overcome these limitations.

Main Methods:

  • Investigated bias in fixed-entry-order re-randomization tests due to non-uniform re-allocation probabilities with unequal allocation.
  • Developed a weighted fixed-entry-order re-randomization test to correct for this bias.
  • Evaluated the proposed test's performance using simulation studies mimicking real clinical trial settings.

Main Results:

  • Identified non-uniform re-allocation probabilities as the source of bias in standard fixed-entry-order re-randomization tests under unequal allocation.
  • Simulation studies demonstrated that the proposed weighted re-randomization test effectively overcomes this bias.
  • The weighted test maintained good performance even in the presence of strong temporal trends.

Conclusions:

  • The weighted fixed-entry-order re-randomization test is a valid and powerful statistical tool for analyzing clinical trials employing minimization with unequal allocation.
  • This method provides a reliable approach to handle temporal trends in such trial designs, enhancing analytical accuracy.