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

Bootstrapping01:24

Bootstrapping

663
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
663
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
Random Sampling Method01:09

Random Sampling Method

12.1K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
12.1K
Binomial Probability Distribution01:15

Binomial Probability Distribution

11.3K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
11.3K
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
Cluster Sampling Method01:20

Cluster Sampling Method

12.5K
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...
12.5K

You might also read

Related Articles

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

Sort by
Same author

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same author

The performance of latent class analysis for clustering multiple long-term conditions is robust to the impact of high prevalence conditions.

Journal of clinical epidemiology·2026
Same author

A randomized controlled Phase I de-escalation trial of molnupiravir and nirmatrelvir/ritonavir combination for mild-moderate SARS-CoV-2 infection.

The Journal of antimicrobial chemotherapy·2026
Same author

The predictive power of first recruits to commercial trial performance.

BMC medical research methodology·2026
Same author

An evaluation of designs for Phase I/IIa dose-finding studies in Tuberculosis.

Statistical methods in medical research·2026
Same author

On the Interplay Between Prior Weight and Variance of the Robustification Component in Robust Mixture Prior Bayesian Dynamic Borrowing Approach.

Statistics in medicine·2026
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Aug 31, 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

42.3K

Bayesian sample size determination in basket trials borrowing information between subsets.

Haiyan Zheng1, Michael J Grayling2, Pavel Mozgunov3

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK and Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK.

Biostatistics (Oxford, England)
|August 22, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian method for sample size calculation in basket trials, enabling information sharing between patient subgroups. This approach significantly reduces the overall sample size needed compared to traditional methods.

Keywords:
Bayesian sample size determinationBorrowing strengthMaster protocolMixture priorPhase II

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
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

Related Experiment Videos

Last Updated: Aug 31, 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

42.3K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
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

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacological Research

Background:

  • Basket trials evaluate new treatments across multiple patient subgroups under a single protocol.
  • Efficient sample size determination is crucial for the success of these complex trials.

Purpose of the Study:

  • To propose a Bayesian approach for sample size determination in randomized basket trials.
  • To enable information borrowing between commensurate patient subgroups to optimize trial efficiency.

Main Methods:

  • Development of closed-form Bayesian sample size formulae for subtrials.
  • Simultaneous solving of subtrial sample sizes based on prespecified commensurability levels.
  • Utilizing a randomized design with treatment and control arms within each subgroup.

Main Results:

  • The Bayesian approach yields comparable sample sizes to frequentist methods when no information is borrowed.
  • Borrowing information between commensurate subgroups significantly reduces the overall required trial sample size.
  • The methodology maintains desired true positive and false positive rates, validated by simulations.

Conclusions:

  • The proposed Bayesian sample size determination is an efficient method for basket trials.
  • Information borrowing can lead to substantial sample size reductions, enhancing trial feasibility.
  • This approach offers a statistically sound framework for designing informative basket trials.