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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

41
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
41
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

127
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
127
Multiple Comparison Tests01:13

Multiple Comparison Tests

3.9K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.9K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

196
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
196
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

169
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
169
Typical Model Studies01:30

Typical Model Studies

359
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
359

You might also read

Related Articles

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

Sort by
Same authorSame journal

Immediate level change estimates can be biased when interrupted time series analyses aggregate over time using segmented linear regression.

Journal of clinical epidemiology·2026
Same author

Diagnostic testing accuracy of DNA methylation tests for detection of high-grade cervical intraepithelial neoplasia and cervical cancer: A systematic review and meta-analysis.

European journal of cancer (Oxford, England : 1990)·2026
Same author

An investigation of discrepancies in outcome reporting and selective reporting bias in interrupted time series studies of health interventions: a methodological study.

BMC public health·2026
Same author

Analysing complex interventions using component network meta-analysis.

BMJ (Clinical research ed.)·2026
Same author

Updating the PRISMA reporting guideline for scoping reviews: a scoping review.

Journal of clinical epidemiology·2026
Same author

Psychosocial interventions for supporting women to stop smoking in pregnancy.

The Cochrane database of systematic reviews·2026

Related Experiment Video

Updated: Jul 2, 2025

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

512

A brief note on the common (fixed)-effect meta-analysis model.

Areti Angeliki Veroniki1, Joanne E McKenzie2

  • 1Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria Street, Toronto, Ontario, Canada; Institute for Health Policy, Management, and Evaluation, University of Toronto, 155 College Street, Toronto, Ontario, Canada.

Journal of Clinical Epidemiology
|February 16, 2024
PubMed
Summary

This meta-analysis focuses on the common-effect model, a key statistical method for combining study results. Understanding its assumptions and methods ensures accurate interpretation of overall findings and confidence intervals.

Keywords:
Common-effectEqual-effectsFixed-effectInverse varianceMantel-HaenszelMeta-analysisPetoSystematic review

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.9K
Meta-Analysis of the Effectiveness and Safety of Shugan Jieyu Capsules for the Treatment of Insomnia
04:34

Meta-Analysis of the Effectiveness and Safety of Shugan Jieyu Capsules for the Treatment of Insomnia

Published on: February 17, 2023

1.1K

Related Experiment Videos

Last Updated: Jul 2, 2025

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

512
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.9K
Meta-Analysis of the Effectiveness and Safety of Shugan Jieyu Capsules for the Treatment of Insomnia
04:34

Meta-Analysis of the Effectiveness and Safety of Shugan Jieyu Capsules for the Treatment of Insomnia

Published on: February 17, 2023

1.1K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Research

Background:

  • Meta-analysis synthesizes findings from multiple studies.
  • Choosing an appropriate statistical model is crucial for accurate meta-analysis.
  • The common-effect model is a prevailing method alongside the random-effects model.

Purpose of the Study:

  • To outline the key assumption of the common-effect model.
  • To describe various common-effect methods (inverse variance, Peto, Mantel-Haenszel).
  • To guide the selection of appropriate methods based on meta-analysis characteristics.

Main Methods:

  • Focus on the common-effect (fixed-effect) model.
  • Description of inverse variance, Peto, and Mantel-Haenszel methods.
  • Demonstration of method application using a dataset.

Main Results:

  • The article details the underlying assumption of the common-effect model.
  • It explains the application and interpretation of different common-effect methods.
  • Guidance is provided on selecting the most suitable method for specific meta-analyses.

Conclusions:

  • Understanding the common-effect model is essential for its appropriate use.
  • Proper application and interpretation of the common-effect model enhance the reliability of meta-analysis results.
  • This analysis aids researchers in selecting and applying common-effect methods effectively.