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

Indicators02:39

Indicators

60.5K
Certain organic substances change color in dilute solution when the hydronium ion concentration reaches a particular value. For example, phenolphthalein is a colorless substance in any aqueous solution with a hydronium ion concentration greater than 5.0 × 10−9 M (pH < 8.3). In more basic solutions where the hydronium ion concentration is less than 5.0 × 10−9 M (pH > 8.3), it is red or pink. Substances such as phenolphthalein, which can be used to determine the pH of a solution, are...
60.5K
Sample Size Calculation01:19

Sample Size Calculation

6.7K
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...
6.7K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.7K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.7K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.1K
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...
4.1K
Cardiovascular Drugs: Classification based on Therapeutic Indications01:18

Cardiovascular Drugs: Classification based on Therapeutic Indications

4.2K
Cardiovascular diseases, encompassing a range of conditions, can significantly affect the heart's operations and the overall circulatory system. These conditions impair the heart's ability to pump blood, leading to a deficit in oxygen supply to crucial organs. Anomalies in the heart's electrical system, known as arrhythmias, can cause heartbeats to accelerate or slow down. Usually, heart rates increase during physical activity and decrease while resting or sleeping. However,...
4.2K
Relative Risk01:12

Relative Risk

2.1K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.1K

You might also read

Related Articles

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

Sort by
Same author

Highly Aqueous Stability of Metal Oxides Encapsulated CsPbBr<sub>3</sub> Nanocrystals via A Generalized Core-Shell Engineering.

Small methods·2026
Same author

Inhibition of Cyclophilin D Rescues Cardiac Function and Bioenergetic Defects Caused by Neonatal Hypoxia.

JACC. Basic to translational science·2026
Same author

Comparison of crestal bone levels, peri-implant indices, mucosal margin position, and pink esthetic score of posterior implant crowns fabricated with immediate vs delayed impressions: A 5-year retrospective cross- sectional study.

Journal of prosthodontics : official journal of the American College of Prosthodontists·2026
Same author

Nitrogen-mediated improvement of ionic homeostasis and antioxidant capacity enhances rice yield and nitrogen use efficiency under soda saline-alkali stress.

Frontiers in plant science·2026
Same author

A Retrospective Single-Center Study of Ferric Derisomaltose in Toddler and School-Age Pediatric Patients.

Pediatric blood & cancer·2026
Same author

Correction to: Targeting PDE4B (Phosphodiesterase-4 Subtype B) for Cardioprotection in Acute Myocardial Infarction via Neutrophils and Microcirculation.

Circulation research·2026

Related Experiment Video

Updated: Jan 28, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.6K

Sample sizes based on three popular indices of risks.

Hongyue Wang1, Bokai Wang1, Xin M Tu2

  • 1Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.

General Psychiatry
|March 1, 2019
PubMed
Summary
This summary is machine-generated.

This study presents a new formula for calculating clinical trial sample sizes for binary outcomes. The formula simplifies sample size determination using absolute risk difference, risk ratio, or odds ratio.

Keywords:
odds ratiorelative riskrisk difference

More Related Videos

Methods for ECG Evaluation of Indicators of Cardiac Risk, and Susceptibility to Aconitine-induced Arrhythmias in Rats Following Status Epilepticus
08:28

Methods for ECG Evaluation of Indicators of Cardiac Risk, and Susceptibility to Aconitine-induced Arrhythmias in Rats Following Status Epilepticus

Published on: April 5, 2011

18.2K
Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
19:15

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

87.6K

Related Experiment Videos

Last Updated: Jan 28, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.6K
Methods for ECG Evaluation of Indicators of Cardiac Risk, and Susceptibility to Aconitine-induced Arrhythmias in Rats Following Status Epilepticus
08:28

Methods for ECG Evaluation of Indicators of Cardiac Risk, and Susceptibility to Aconitine-induced Arrhythmias in Rats Following Status Epilepticus

Published on: April 5, 2011

18.2K
Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
19:15

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

87.6K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Epidemiology

Background:

  • Accurate sample size calculation is essential for the validity and efficiency of clinical trials.
  • Existing methods for sample size determination can be complex, particularly when using different risk indices.

Purpose of the Study:

  • To derive a novel, simplified formula for calculating sample size in clinical trials with binary outcomes.
  • To provide a unified approach for sample size estimation based on common risk difference indices.

Main Methods:

  • Derivation of a new mathematical formula for sample size calculation.
  • Utilizing absolute risk difference as the foundational index.
  • Demonstrating the derivation of sample size formulas for risk ratio and odds ratio from the absolute difference formula.

Main Results:

  • A new, fundamental formula for sample size calculation based on absolute risk difference was derived.
  • This formula facilitates straightforward calculation of sample sizes for binary outcomes using risk ratio or odds ratio.

Conclusions:

  • The newly derived formula offers a more accessible method for sample size justification in clinical trial design.
  • This approach enhances the practicality of sample size planning, particularly for binary outcome studies.