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

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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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, controlled...
Clinical Trials01:16

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
Clinical Trials: Overview01:11

Clinical Trials: Overview

Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Study Design in Statistics01:15

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...

You might also read

Related Articles

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

Sort by
Same author

Integrative proteogenomic analyses identify plasma proteins that impact the risk of ischemic stroke.

Communications medicine·2026
Same author

Effect of pH and Benzalkonium Chloride in Low-Dose Atropine on Pupil Dilation in Dutch Belted Rabbits.

Journal of ocular pharmacology and therapeutics : the official journal of the Association for Ocular Pharmacology and Therapeutics·2026
Same author

Multi-cohort proteogenomic analyses reveal genetic effects across the proteome and diseasome.

Cell·2026
Same author

Half Of Rural Patients Bypassed Their Local Hospital But Received Lower-Quality Surgical Care Elsewhere, 2016-20.

Health affairs (Project Hope)·2026
Same author

AI-driven reclassification of multiple sclerosis progression.

Nature medicine·2025
Same author

Safety and efficacy of HH2853, a novel EZH1/2 dual inhibitor, in patients with refractory solid tumours or non-Hodgkin lymphomas: a phase I study.

EClinicalMedicine·2025

Related Experiment Video

Updated: Jul 9, 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

Bayesian sample size for exploratory clinical trials incorporating historical data.

John Whitehead1, Elsa Valdés-Márquez, Patrick Johnson

  • 1Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK. j.whitehead@lancaster.ac.uk

Statistics in Medicine
|December 12, 2007
PubMed
Summary
This summary is machine-generated.

This study introduces a straightforward Bayesian method for determining clinical trial sample sizes. It ensures trials yield convincing evidence for treatment efficacy or clinically relevant differences, aligning with frequentist methods.

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

Related Experiment Videos

Last Updated: Jul 9, 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

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Determining adequate sample size is crucial for clinical trials to yield statistically significant and clinically meaningful results.
  • Existing methods for sample size determination may not fully capture Bayesian principles or specific hypothesis testing scenarios.

Purpose of the Study:

  • To present a simple Bayesian approach for sample size determination in clinical trials.
  • To ensure collected data provides convincing evidence for treatment superiority or a clinically relevant lack of effect.

Main Methods:

  • The proposed Bayesian method is introduced using a single stream of binary observations.
  • It extends to comparing two independent streams of normally distributed responses, considering known and unknown common variances.
  • The approach is further explored for large samples analyzed using score statistic properties.

Main Results:

  • The Bayesian approach resembles standard frequentist sample size calculations.
  • Under specific conditions with non-informative priors, it yields identical results to frequentist methods.
  • The method incorporates an alternative hypothesis specifying a clinically relevant difference in treatment effect.

Conclusions:

  • The presented Bayesian framework offers a flexible and robust method for sample size determination in clinical trials.
  • It provides a unified approach applicable to various data types and statistical models.
  • This method enhances the rigor of clinical trial design by integrating Bayesian principles with practical considerations.