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

11.0K
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...
11.0K
Sample Size Calculation01:19

Sample Size Calculation

5.2K
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...
5.2K
Sampling Plans01:23

Sampling Plans

1.4K
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...
1.4K
Randomized Experiments01:13

Randomized Experiments

6.3K
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...
6.3K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

627
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
627
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

407
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...
407

You might also read

Related Articles

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

Sort by
Same author

Sample size determination for hypothesis testing on the intraclass correlation coefficient in a two-way analysis of variance model.

The British journal of mathematical and statistical psychology·2025
Same author

Efficient design of cluster randomized trials and individually randomized group treatment trials.

Psychological methods·2025
Same author

Widening the Price Gap: The Effect of The Netherlands' 2020 Tax Increase on Tobacco Prices.

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco·2024
Same author

Tax increases as an incentive to quit smoking: is thinking about quitting due to a tobacco tax increase associated with post-tax increase smoking cessation?

BMC public health·2024
Same author

Real-Life Effectiveness of Smoking Cessation Delivery Modes: A Comparison Against Telephone Counseling and the Role of Individual Characteristics and Health Conditions in Quit Success.

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco·2023
Same author

Best (but oft forgotten) practices: Efficient sample sizes for commonly used trial designs.

The American journal of clinical nutrition·2023

Related Experiment Video

Updated: Apr 27, 2026

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

1.4K

Sample size calculation in cost-effectiveness cluster randomized trials: optimal and maximin approaches.

Md Abu Manju, Math J J M Candel, Martijn P F Berger

    Statistics in Medicine
    |July 15, 2014
    PubMed
    Summary

    This study determines optimal sample sizes for cluster randomized trials to assess cost-effectiveness. It introduces maximin sample sizes that are robust to certain parameter misspecifications, improving trial planning.

    More Related Videos

    Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
    10:26

    Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

    Published on: September 11, 2021

    3.1K
    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

    14.3K

    Related Experiment Videos

    Last Updated: Apr 27, 2026

    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

    1.4K
    Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
    10:26

    Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

    Published on: September 11, 2021

    3.1K
    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

    14.3K

    Area of Science:

    • Biostatistics
    • Health Economics
    • Clinical Trials

    Background:

    • Cluster randomized trials (CRTs) are essential for evaluating interventions in group-level settings.
    • Assessing the cost-effectiveness of treatments requires careful sample size calculation.
    • Standard methods often lack robustness when planning parameters are uncertain.

    Purpose of the Study:

    • To derive optimal and maximin sample sizes for CRTs evaluating cost-effectiveness on a continuous scale.
    • To provide methods for maximizing power or efficiency within budget constraints.
    • To address the challenge of unknown model parameters during study planning.

    Main Methods:

    • Formulating sample size calculations for optimal efficiency/power or minimal cost.
    • Developing maximin sample size strategies to enhance robustness against parameter misspecification.
    • Investigating the impact of intra-cluster correlations (ICCs), cost-effect correlations, and variance ratios on sample size determination.

    Main Results:

    • Optimal sample sizes depend on ICCs, cost-effect correlations, variance ratio, costs, and budget.
    • Maximin sample sizes offer robustness against misspecified cost-effect correlations.
    • Robustness of maximin sizes to ICC misspecification varies with the variance ratio.

    Conclusions:

    • Maximin sample sizes provide a more reliable approach for planning CRTs when parameters are uncertain.
    • Careful consideration of the variance ratio is crucial for the robustness of maximin sample sizes.
    • The methods presented enable sufficient power for cost-effectiveness analyses in CRTs.