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...
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...
Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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:
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...

You might also read

Related Articles

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

Sort by
Same author

Repetitive Transcranial Magnetic Stimulation as a Cognitive Rehabilitation Approach for Veterans With Parkinson Disease and Mild Cognitive Impairment: Protocol for a Randomized Sham-Controlled Trial.

JMIR research protocols·2026
Same author

Bridging technology and education: The use of ChatGPT in grading pharmacy student exams.

Currents in pharmacy teaching & learning·2026
Same author

Prospective Assessment of Depression and Anxiety Trajectories Among Emergency Department Patients with Somatic Complaints.

The western journal of emergency medicine·2026
Same author

Content Validity of the K-CAT<sup>®</sup> Assessing Mental Health Challenges in Autism: A Mixed Methods Analysis of Perspectives from Autistic Youth, Caregivers, and Clinicians.

Journal of autism and developmental disorders·2026
Same author

Gut-derived metabolic reprogramming drives immune aging and tissue degeneration.

bioRxiv : the preprint server for biology·2026
Same author

A novel strategy for detecting multiple mediators in high-dimensional mediation models.

Frontiers in psychiatry·2025
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
Same journal

Subgroup Analysis of Interval-censored Failure Time Data With Application to Alzheimer's Disease.

Statistics in medicine·2026
Same journal

Rejoinder to Commentaries on "A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events".

Statistics in medicine·2026
Same journal

A Multi-Stage Drop-the-Loser Design With Superiority Boundaries.

Statistics in medicine·2026
Same journal

Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced Adaptive Sampling.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: May 12, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Sample size determination for clustered count data.

Anup Amatya1, Dulal Bhaumik, Robert D Gibbons

  • 1Department of Health Science, New Mexico State University, Las Cruces, NM, USA.

Statistics in Medicine
|April 17, 2013
PubMed
Summary
This summary is machine-generated.

Determining sample size for count data in cluster randomized trials is crucial. This study offers new, simple formulas for calculating the number of clusters needed, improving accuracy for various study designs.

Keywords:
Poisson regressioncluster randomizedgeneralized estimating equationsmultisite

More Related Videos

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone (ITZ)
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone (ITZ)

Published on: December 16, 2019

Related Experiment Videos

Last Updated: May 12, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone (ITZ)
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone (ITZ)

Published on: December 16, 2019

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Multicenter or cluster randomized clinical trials commonly involve count data, with patients nested within research centers.
  • Accurate sample size determination is essential for the statistical power and validity of these trials.
  • Existing methods may not be optimal for all cluster randomized designs and data types.

Purpose of the Study:

  • To develop and present simple, closed-form expressions for sample size determination in cluster randomized trials with count data.
  • To compare the performance of proposed methods against existing approaches for both cross-sectional and longitudinal studies.
  • To provide practical tools for researchers designing multicenter clinical trials.

Main Methods:

  • Derivation of simple expressions for calculating the number of clusters based on between-cluster variation and intercluster correlation for cross-sectional studies.
  • Application of cluster-specific (generalized mixed-effect regression) and population-averaged (generalized estimating equations) estimators.
  • Development of a versatile method for longitudinal studies.
  • Theoretical and numerical comparisons with existing sample size determination methods.

Main Results:

  • The proposed methods provide simple, closed-form solutions for sample size calculations in cross-sectional studies.
  • The performance of the proposed method is superior for subject-level randomized designs.
  • For cluster-level randomized designs, the comparative performance is dependent on the rate ratio.
  • A versatile method is presented for longitudinal count data.

Conclusions:

  • The study offers improved and practical methods for sample size determination in cluster randomized trials with count data.
  • The derived expressions simplify calculations, particularly for cross-sectional designs.
  • The findings guide the selection of appropriate sample size methods based on study design and data characteristics.