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...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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...
Randomized Experiments01:13

Randomized Experiments

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...
Systematic Sampling Method01:17

Systematic Sampling Method

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.
Systematic sampling is one of the simplest methods...

You might also read

Related Articles

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

Sort by
Same author

Polypill for heart failure with reduced ejection fraction: the POLY-HF randomized trial.

Nature medicine·2026
Same author

Stress and cardiovascular disease: Testing a vigilance pathway in two contemporary theories.

Health psychology : official journal of the Division of Health Psychology, American Psychological Association·2026
Same author

Monoclonal antibodies targeting PCDH7 inhibit tumor growth and enhance immune responses in <i>KRAS</i>-mutant non-small cell lung cancer.

Science advances·2026
Same author

Involved nodal versus elective neck radiotherapy (INVERT) for head and neck squamous cell carcinoma: a prospective phase II randomized controlled trial protocol.

BMC cancer·2026
Same author

Sample Size Determination for Comparing Slopes in Cluster Randomized Trials With Longitudinal Measurements.

Statistics in medicine·2026
Same author

Undetected Weight Loss Associates With Upstaging in Cancer Patients.

Journal of cachexia, sarcopenia and muscle·2026
Same journal

A statistical evaluation of decision-making methods and the efficiency of Bayesian multi-arm multi-stage trials.

Clinical trials (London, England)·2026
Same journal

Accounting for non-adherence: A re-analysis of the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results trial.

Clinical trials (London, England)·2026
Same journal

Phase I design for partially ordered groups with late-onset toxicity.

Clinical trials (London, England)·2026
Same journal

Trial informed consent forms, the Declaration of Helsinki and the SPIRIT 2025 statement.

Clinical trials (London, England)·2026
Same journal

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Morning panel discussion).

Clinical trials (London, England)·2026
Same journal

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Afternoon panel discussion).

Clinical trials (London, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2026

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

Calculating sample size in trials using historical controls.

Song Zhang1, Jing Cao, Chul Ahn

  • 1Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, USA. song.zhang@utsouthwestern.edu

Clinical Trials (London, England)
|June 25, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new sample size formula for historical control trials that accounts for data variability. It ensures reliable power and type I error control by focusing on percentiles rather than means.

More Related Videos

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Related Experiment Videos

Last Updated: Jun 12, 2026

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

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • The Makuch-Simon formula for historical control trials assumes a fixed control effect, ignoring inherent data uncertainty.
  • This assumption leads to inaccurate power and type I error rates in practice.
  • Previous methods fail to account for the randomness in historical control data.

Purpose of the Study:

  • To develop a robust sample size formula for historical control trials.
  • To accurately address the randomness and uncertainty in historical control group observations.
  • To ensure reliable statistical power and type I error control.

Main Methods:

  • Investigated the skewed distributions of power and type I error across historical control data realizations.
  • Derived a novel sample size formula controlling specific percentiles of power and type I error.
  • Validated the approach using simulations with unknown population variances.

Main Results:

  • A closed-form sample size formula was developed for historical control trials.
  • The formula effectively controls arbitrary percentiles of power and type I error.
  • Simulations confirmed the formula's validity, even with unknown population variances.

Conclusions:

  • A new sample size formula is presented for historical control trials with continuous outcomes.
  • This formula addresses the skewed distributions of power and type I error.
  • The method provides more reliable statistical planning compared to prior approaches.