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

Cluster Sampling Method01:20

Cluster Sampling Method

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...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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...

You might also read

Related Articles

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

Sort by
Same author

One-year impact on HPV vaccination coverage of a school- and primary care-based intervention: the PrevHPV cluster randomised trial.

Scientific reports·2026
Same author

Combined Oral Ivermectin and 5% Permethrin Cream to Treat Severe Scabies.

The New England journal of medicine·2026
Same author

Identifying cancer in the French National Health Data System (SNDS): an updated scoping review of algorithms, validation and applications.

Journal of epidemiology and population health·2026
Same author

Prevention of secondary infections by interferon-gamma in ICU-acquired sustained immune suppression in France: study protocol of the PLATINIUM randomised trial.

BMJ open·2026
Same author

Inter-physician heterogeneity in colorectal cancer screening participation in France: a study based on the French National Health Data System.

Archives of public health = Archives belges de sante publique·2026
Same author

Consensus for the most suitable trial design to assess therapy for rare vascular malformations: a Delphi study.

Scientific reports·2026

Related Experiment Video

Updated: Jun 14, 2026

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
04:53

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

Planning a cluster randomized trial with unequal cluster sizes: practical issues involving continuous outcomes.

Lydia Guittet1, Philippe Ravaud, Bruno Giraudeau

  • 1Département d'Epidémiologie, Biostatistique et Recherche Clinique, Groupe Hospitalier Bichat-Claude Bernard (AP-HP), Université Xavier Bichat, Paris, France. guittetl@free.fr

BMC Medical Research Methodology
|April 14, 2006
PubMed
Summary

Cluster size imbalance significantly impacts trial power, especially with few clusters or high correlation. The minimum variance weights correction effectively addresses severe imbalances in sample size calculations.

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

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

Related Experiment Videos

Last Updated: Jun 14, 2026

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
04:53

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

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

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

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Health Services Research

Background:

  • Cluster randomization is common for health, screening, and educational interventions.
  • Sample size calculations often overlook potential cluster size imbalances.
  • Significant discrepancies in cluster sizes can occur.

Purpose of the Study:

  • To investigate the impact of cluster size imbalance on statistical power.
  • To evaluate methods for adapting sample size calculations to account for cluster size imbalance.
  • To identify strategies for achieving adequately powered trials with variable cluster sizes.

Main Methods:

  • Conducted simulation studies to assess power under varying cluster size imbalances.
  • Evaluated four proposed methods for adjusting sample size calculations.
  • Focused on scenarios with low cluster numbers and high intraclass correlation coefficients.

Main Results:

  • Severe cluster size imbalance can substantially reduce trial power.
  • The effect is more pronounced with fewer clusters and higher intraclass correlation.
  • Minimum variance weights correction demonstrated superior performance in sample size calculations for imbalanced clusters.

Conclusions:

  • Reporting cluster sizes is crucial for evaluating trial power and informing future designs.
  • An adapted variation inflation factor (VIF) using minimum variance weights is proposed for pre-specified imbalances.
  • This adaptation is applicable when imbalance can be defined by recruitment proportions (gamma and tau).