Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

8.8K
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...
8.8K
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

215
Body: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...
215
Study Design in Statistics01:15

Study Design in Statistics

9.9K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
9.9K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

542
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
542
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

169
Body: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...
169
Crossover Experiments01:16

Crossover Experiments

4.5K
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.
4.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Correction: Platelet-membrane-biomimetic nanoparticles for targeted antitumor drug delivery.

Journal of nanobiotechnology·2026
Same author

Linking GWAS risk genes to transcriptional features of major depressive disorder via in vivo Perturb-seq.

Nature genetics·2026
Same author

Copper nanoregulator with organelle-level precision reprograms COMMD1-Mediated copper homeostasis for myocardial infarction repair.

Biomaterials·2026
Same author

Childhood violence exposure and the risk of multimorbidity in middle-aged and older adults: A multi-cohort study.

Journal of affective disorders·2026
Same author

From radical/electron competition to interfacial shielding: How PFOA and its derivatives obstruct electrochemical degradation of microplastics.

Journal of hazardous materials·2026
Same author

Correction: Functionalized boron nanosheets as an intelligent nanoplatform for synergistic low-temperature photothermal therapy and chemotherapy.

Nanoscale·2026
Same journal

A New Estimation Algorithm for Destructive Cure Model: Illustration with Exponentially Weighted Poisson Competing Risks.

Communications in statistics: Simulation and computation·2026
Same journal

Simulating survival data with predefined censoring rates under a mixture of non-informative right censoring schemes.

Communications in statistics: Simulation and computation·2026
Same journal

Sampling Spiked Wishart Eigenvalues.

Communications in statistics: Simulation and computation·2025
Same journal

Likelihood-Based Inference for Semi-Parametric Transformation Cure Models with Interval Censored Data.

Communications in statistics: Simulation and computation·2025
Same journal

Bayesian variable selection for logistic regression with a differentially misclassified binary covariate.

Communications in statistics: Simulation and computation·2025
Same journal

Statistical methods for assessing treatment effects on ordinal outcomes using observational data.

Communications in statistics: Simulation and computation·2025
See all related articles

Related Experiment Video

Updated: Jan 11, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K

BayCAR: A Bayesian based Covariate-Adaptive Randomization method for multi-arm trials.

Shengping Yang1, Jianrong Wu2

  • 1Department of Biostatistics, Pennington Biomedical Research Center, Baton Rouge, LA.

Communications in Statistics: Simulation and Computation
|November 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian covariate-adaptive randomization method for clinical trials. This approach effectively balances numerous covariates, improving trial design and reliability.

Keywords:
Adaptive randomizationBayesian designcontinuous outcomecovariate-adaptivecovariate-adjusted

More Related Videos

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

15.0K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.1K

Related Experiment Videos

Last Updated: Jan 11, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K
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

15.0K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.1K

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Bayesian Inference

Background:

  • Randomization is crucial for controlled clinical trials to prevent confounding.
  • Existing methods like restricted randomization and minimization have limitations, especially with many covariates.
  • Minimization methods require further theoretical justification for their adaptive randomization probability.

Purpose of the Study:

  • To propose a novel Bayesian covariate-adaptive randomization method.
  • To provide meaningful interpretations for adaptive randomization probabilities.
  • To achieve balanced distributions of numerous categorical and continuous covariates across treatment arms.

Main Methods:

  • Development of a Bayesian framework for covariate-adaptive randomization.
  • Incorporation of adaptive randomization probabilities with clear interpretations.
  • Application to scenarios requiring the balance of a large number of covariates.

Main Results:

  • The proposed method demonstrates desirable marginal and overall covariate balance.
  • Effective balancing is achieved for both categorical and continuous covariates.
  • The method is particularly advantageous when dealing with a large number of covariates.

Conclusions:

  • The Bayesian covariate-adaptive randomization method offers a robust solution for complex clinical trial designs.
  • It provides interpretable adaptive probabilities and superior covariate balancing.
  • This method enhances the reliability and validity of controlled clinical trials with many covariates.