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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...
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...
Crossover Experiments01:16

Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...

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Related Experiment Video

Updated: Jun 26, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Permutation test following covariate-adaptive randomization in randomized controlled trials.

Takahiro Hasegawa1, Toshiro Tango

  • 1Biostatistics Department, Shionogi & Co., Ltd., Osaka, Japan. takahiro.hasegawa@shionogi.co.jp

Journal of Biopharmaceutical Statistics
|January 8, 2009
PubMed
Summary
This summary is machine-generated.

Permutation tests, when covariate balance is achieved, match analysis of covariance. This covariate-adaptive randomization offers a robust alternative for confirmatory randomized controlled trials.

Related Experiment Videos

Last Updated: Jun 26, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Analysis

Background:

  • Randomized controlled trials (RCTs) involve patient allocation, not random sampling from a population.
  • Traditional statistical analyses may not fully leverage the randomization model.
  • The design and analysis should consider the specific nature of randomization.

Purpose of the Study:

  • To theoretically demonstrate the equivalence between a permutation test and analysis of covariance under specific conditions.
  • To propose and evaluate a permutation test using covariate-adaptive randomization as an alternative to traditional methods in RCTs.
  • To illustrate the application of the proposed method using real-world clinical trial data.

Main Methods:

  • Theoretical derivation showing the identity of permutation tests and ANCOVA with perfect covariate balance.
  • Monte Carlo simulation study to assess the performance of the proposed permutation test.
  • Application of the permutation test to modified data from a pirfenidone RCT.

Main Results:

  • A permutation test based on the difference in means is theoretically identical to analysis of covariance when marginal covariate balance is fully achieved.
  • The permutation test following Pocock-Simon's covariate-adaptive randomization performs well.
  • The proposed method is a viable alternative to traditional population-based tests in confirmatory RCTs.

Conclusions:

  • Covariate-adaptive randomization combined with permutation tests provides a powerful and valid approach for confirmatory RCTs.
  • This method is particularly useful when dealing with important prognostic factors.
  • The study offers a practical and theoretically sound alternative for clinical trial analysis.