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

Sample Size Calculation01:19

Sample Size Calculation

3.7K
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...
3.7K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.4K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.4K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.4K
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...
3.4K
Bonferroni Test01:10

Bonferroni Test

2.8K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.8K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

157
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,...
157
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.2K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.2K

You might also read

Related Articles

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

Sort by
Same author

It's about time: The association between abacavir and cardiovascular disease.

Antiviral therapy·2026
Same author

AI Methods for Implementation Science (AIM-IS): developing a framework, toolkit, and reporting standard for the responsible use of AI in implementation practice and research.

Implementation science : IS·2026
Same author

Revisiting the Meaning of 'Value' in Value-Based Healthcare: A Concept Analysis.

Journal of advanced nursing·2026
Same author

Tracking the Evolving Role of Artificial Intelligence in Implementation Science: Protocol for a Living Scoping Review of Applications, Evaluation Approaches and Outcomes.

F1000Research·2026
Same author

Standing the test of time: diagnostic accuracy of the Edmonton Symptom Assessment System-Revised (ESAS-r) for anxiety and depression screening.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026
Same author

Implementation strategies for embedding patient-reported outcome and experience measures (PROMs/PREMs) in routine care: secondary analysis of an umbrella review.

Journal of patient-reported outcomes·2026

Related Experiment Video

Updated: Aug 17, 2025

Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

42.2K

Bayesian sample size calculations for comparing two strategies in SMART studies.

Armando Turchetta1, Erica E M Moodie1, David A Stephens2

  • 1Department of Epidemiology, Biostatistics and Occupational Health, Montreal, Quebec, Canada.

Biometrics
|December 13, 2022
PubMed
Summary

This study introduces a Bayesian approach for sample size calculations in sequential multiple assignment randomized trials (SMARTs). This method provides more robust estimates for adaptive treatment strategies (ATSs) by reducing reliance on assumptions and incorporating prior knowledge.

Keywords:
Bayesian trial designadaptive treatment strategiessample sizesequential multiple assignment randomized trial

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

4.0K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

17.9K

Related Experiment Videos

Last Updated: Aug 17, 2025

Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

42.2K
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

4.0K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

17.9K

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Health Services Research

Background:

  • Adaptive treatment strategies (ATSs) offer personalized management for chronic conditions lacking universal treatments.
  • Sequential multiple assignment randomized trials (SMARTs) are increasingly used to study ATSs.
  • Frequentist sample size calculations for SMARTs often rely on rigid assumptions, risking power inadequacy.

Purpose of the Study:

  • To develop and evaluate a Bayesian methodology for sample size calculations in SMARTs.
  • To provide more realistic and robust sample size estimates by accounting for input uncertainty.
  • To offer an alternative to frequentist methods that require fewer assumptions and can integrate prior knowledge.

Main Methods:

  • A Bayesian framework utilizing the 'two priors' approach for sample size estimation in SMARTs.
  • Comparison of Bayesian calculations with standard frequentist formulae.
  • Simulation studies to evaluate the proposed methodology's performance.
  • Application to estimate sample size for a SMART of an internet-based intervention for cardiovascular disease patients.

Main Results:

  • The Bayesian approach allows for more realistic estimates by incorporating uncertainty in key parameters.
  • This methodology reduces reliance on pre-specified interim response rates and variance components.
  • The focus shifts from standardized effect size to the Minimum Detectable Difference (MDD).

Conclusions:

  • Bayesian sample size calculations offer a more flexible and robust alternative for designing SMARTs.
  • This approach enhances the reliability of power calculations and accommodates prior information effectively.
  • The methodology is suitable for planning adaptive clinical trials, including those for digital health interventions.