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

9.3K
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...
9.3K
Crossover Experiments01:16

Crossover Experiments

4.7K
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.
4.7K
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

386
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...
386
Cluster Sampling Method01:20

Cluster Sampling Method

15.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.6K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

705
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...
705
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

368
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...
368

You might also read

Related Articles

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

Sort by
Same authorSame journal

Immediate level change estimates can be biased when interrupted time series analyses aggregate over time using segmented linear regression.

Journal of clinical epidemiology·2026
Same author

SPIRIT 2025 statement: updated guideline for protocols of randomised trials.

Lancet (London, England)·2026
Same author

The CRT-Estimands Framework for Cluster Randomized Trials.

JAMA·2026
Same author

CRT-Estimands Framework: consensus based extension of the ICH E9(R1) addendum for cluster randomised trials.

BMJ (Clinical research ed.)·2026
Same author

An investigation of discrepancies in outcome reporting and selective reporting bias in interrupted time series studies of health interventions: a methodological study.

BMC public health·2026
Same author

Tools to help patients and other stakeholders' input into choice of intercurrent event strategy for estimands in randomised trials.

Trials·2026

Related Experiment Video

Updated: Mar 29, 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

2.7K

Appropriate statistical methods were infrequently used in cluster-randomized crossover trials.

Sarah J Arnup1, Andrew B Forbes1, Brennan C Kahan2

  • 1School of Public Health and Preventive Medicine, The Alfred Centre, Monash University, 99 Commercial Road, Melbourne, Victoria 3004, Australia.

Journal of Clinical Epidemiology
|December 4, 2015
PubMed
Summary

This review found that cluster-randomized crossover (CRXO) trials often lack appropriate sample size calculations and statistical analysis methods. Improved reporting and application of these methods are needed for CRXO trial validity.

Keywords:
ClusterCluster-randomized crossover trialCrossoverDesignSample sizeStatistical analysis

More Related Videos

Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health
06:13

Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health

Published on: December 1, 2023

1.9K
Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
06:18

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR

Published on: July 11, 2025

1.0K

Related Experiment Videos

Last Updated: Mar 29, 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

2.7K
Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health
06:13

Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health

Published on: December 1, 2023

1.9K
Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
06:18

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR

Published on: July 11, 2025

1.0K

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Epidemiology

Background:

  • Cluster-randomized crossover (CRXO) trials are complex designs used in various health research fields.
  • Assessing the methodological rigor of these trials is crucial for reliable evidence generation.

Purpose of the Study:

  • To systematically review the design characteristics and statistical methods employed in published CRXO trials.
  • To identify common practices and areas for improvement in CRXO trial methodology.

Main Methods:

  • A systematic literature search of major databases (MEDLINE, EMBASE, CINAHL Plus) was conducted up to December 2014.
  • Data on trial design, sample size calculation, data analysis, and handling of missing data were extracted.
  • Citation searches of methodological articles were also performed.

Main Results:

  • Ninety-one CRXO trials with 139 endpoint analyses were included.
  • Sample size justifications were provided in 58% of trials, but only 10% appropriately accounted for the CRXO design.
  • Cluster-level analyses were more likely to be appropriate (10/12) than individual-level analyses (4/127).

Conclusions:

  • There is a significant need to improve the application and reporting of appropriate statistical methods in CRXO trials.
  • Enhancements in sample size calculation and data analysis techniques are recommended.
  • Future CRXO trials should adhere to rigorous methodological standards for robust findings.