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

Statistical Significance01:50

Statistical Significance

22.2K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
22.2K
Fixed Action Patterns01:06

Fixed Action Patterns

17.7K
A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
17.7K
Probability in Statistics01:14

Probability in Statistics

23.5K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
23.5K
Introduction to Statistics01:17

Introduction to Statistics

64.4K
The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...
64.4K
Random Error01:04

Random Error

9.8K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.8K
Random Variables01:09

Random Variables

17.9K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.9K

You might also read

Related Articles

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

Sort by
Same author

Great debate: surgical aortic valve replacement is first choice for aortic stenosis in patients with a life expectancy beyond 5 years.

European heart journal·2026
Same author

Systematic Review and Reconstructed Individual Patient Data of Atherosclerotic Subclavian Artery Aneurysm in the Endovascular Era.

Annals of vascular surgery·2026
Same author

Manufacturing 3D aortic root models for in vitro assessment of calcific aortic valve stenosis.

Annals of biomedical engineering·2026
Same author

Interaction between alveolar diffusing capacity and B-lines in heart failure with preserved ejection fraction - A non-invasive exercise study.

International journal of cardiology·2026
Same author

[2025 ESC/EACTS Guidelines for the management of valvular heart disease].

Giornale italiano di cardiologia (2006)·2025
Same author

Reply to Matsubara.

European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery·2025
Same journal

Association of liver dysfunction with outcomes after cardiac surgery-a meta-analysis.

Interactive cardiovascular and thoracic surgery·2022
Same journal

Extrapleural approach for thoracoabdominal infected aortic endograft: surgical and circulatory strategies.

Interactive cardiovascular and thoracic surgery·2022
Same journal

Retraction: Virtual reality-guided aortic valve leaflet reconstruction for type 0 bicuspid aortic stenosis.

Interactive cardiovascular and thoracic surgery·2022
Same journal

Continuous vagal intraoperative neuromonitoring during video-assisted thoracoscopic surgery for left lung cancer: its efficacy in preventing permanent vocal cord paralysis.

Interactive cardiovascular and thoracic surgery·2022
Same journal

Postoperative aortic injury caused by a staple line formed during wedge resection of the lung.

Interactive cardiovascular and thoracic surgery·2022
Same journal

Surgical management of cardiac cystic echinococcosis in a paediatric patient: a case report.

Interactive cardiovascular and thoracic surgery·2022
See all related articles

Related Experiment Video

Updated: Feb 9, 2026

Primer-Free Aptamer Selection Using A Random DNA Library
11:14

Primer-Free Aptamer Selection Using A Random DNA Library

Published on: July 26, 2010

25.4K

Statistical Primer: heterogeneity, random- or fixed-effects model analyses?

Fabio Barili1, Alessandro Parolari2, Pieter A Kappetein3

  • 1Department of Cardiac Surgery, S. Croce Hospital, Cuneo, Italy.

Interactive Cardiovascular and Thoracic Surgery
|June 6, 2018
PubMed
Summary
This summary is machine-generated.

Heterogeneity in meta-analysis refers to variations in treatment effects across studies. Understanding and quantifying this variation is crucial for choosing appropriate statistical models like random-effects models.

More Related Videos

Laboratory Protocol for Genetic Gut Content Analyses of Aquatic Macroinvertebrates Using Group-specific rDNA Primers
10:17

Laboratory Protocol for Genetic Gut Content Analyses of Aquatic Macroinvertebrates Using Group-specific rDNA Primers

Published on: October 5, 2017

9.3K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K

Related Experiment Videos

Last Updated: Feb 9, 2026

Primer-Free Aptamer Selection Using A Random DNA Library
11:14

Primer-Free Aptamer Selection Using A Random DNA Library

Published on: July 26, 2010

25.4K
Laboratory Protocol for Genetic Gut Content Analyses of Aquatic Macroinvertebrates Using Group-specific rDNA Primers
10:17

Laboratory Protocol for Genetic Gut Content Analyses of Aquatic Macroinvertebrates Using Group-specific rDNA Primers

Published on: October 5, 2017

9.3K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Heterogeneity in meta-analysis signifies variations in treatment effects beyond chance.
  • This variation necessitates specific statistical methods for data summarization.

Purpose of the Study:

  • To explain the concept of heterogeneity in meta-analysis.
  • To highlight the importance of random-effects models when heterogeneity is present.
  • To introduce key statistics used for quantifying heterogeneity.

Main Methods:

  • Discusses the preference for random-effects models over fixed-effects models when heterogeneity is anticipated.
  • Explains that random-effects models account for variability in true treatment effects across studies.
  • Details the components of confidence intervals in random-effects models, including within-study and between-study variance.

Main Results:

  • Identifies five key statistics for assessing heterogeneity: Q statistic, its P-value, T2, T, and I2.
  • Explains that Q statistic and P-value test for homogeneity.
  • T2, T, and I2 quantify the extent and proportion of heterogeneity.

Conclusions:

  • Random-effects models provide summary effects and confidence intervals that reflect observed heterogeneity.
  • These estimates offer practical implications and can be compared to fixed-effects estimates.
  • Understanding heterogeneity is vital for accurate interpretation of meta-analysis results.