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 Experiment Video

Updated: Apr 11, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.1K

Robust variance estimation in meta-regression with binary dependent effects.

Elizabeth Tipton1

  • 1Teachers College, Columbia University, New York, NY, USA.

Research Synthesis Methods
|June 9, 2015
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

587
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...
587
Multiple Regression01:25

Multiple Regression

4.4K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.4K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

717
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
717
Variation01:19

Variation

8.4K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.4K
Regression Analysis01:11

Regression Analysis

9.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
9.0K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.9K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.9K

You might also read

Related Articles

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

Sort by
Same author

A preliminary data analysis workflow for meta-analysis of dependent effect sizes.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

What can we learn from 1,000 meta-analyses across 10 different disciplines?

Research synthesis methods·2026
Same author

Meta-analytic rain cloud plots: Improving evidence communication through data visualization design principles.

Research synthesis methods·2026
Same author

Case Report: An exploration of the neurodevelopmental phenotype of five patients with 48,XXYY during early childhood years.

Frontiers in endocrinology·2026
Same author

Prophylactic Antiseizure Medication in Patients with Lobar Intracerebral Hemorrhage.

Neurocritical care·2025
Same author

Precision ecology for targeted conservation action.

Nature ecology & evolution·2025
Same journal

Prompt engineering of large language models for paper screening in medical meta-analyses and systematic reviews: A prospective comparative study - CORRIGENDUM.

Research synthesis methods·2026
Same journal

Evaluating the accuracy and speed of eight deduplication tools: A comparative study.

Research synthesis methods·2026
Same journal

A comparison of preprint search aggregators: comprehensive identification of preprints in the information retrieval stage of evidence syntheses.

Research synthesis methods·2026
Same journal

Meta-research on key metrics of preregistered scoping reviews.

Research synthesis methods·2026
Same journal

Facilitators and barriers to engaging patient partners in knowledge syntheses: A stage-based approach.

Research synthesis methods·2026
Same journal

Response to: Five methodological considerations for validating LLMs in risk of bias assessment.

Research synthesis methods·2026
See all related articles

A robust variance estimation method effectively handles dependent effect sizes in meta-analysis, even with small sample sizes. This method shows good performance for risk difference, log risk ratio, and log odds ratio estimations.

Area of Science:

  • Biostatistics
  • Meta-analysis
  • Statistical Methods

Background:

  • Dependent effect size estimates pose a significant challenge in meta-analysis.
  • This issue commonly occurs with nested data or multiple measures from the same subjects.
  • A robust variance estimation method has been developed to address non-independent effect sizes.

Purpose of the Study:

  • To evaluate the robustness of the robust variance estimation method in small samples.
  • To assess the accuracy of 95% confidence intervals using this estimator for specific effect sizes (risk difference, log risk ratio, log odds ratio).
  • To examine performance for both mean effect (intercept) and slope estimations.

Main Methods:

  • A simulation study was conducted to investigate the method's performance.
Keywords:
binary outcomesdependent effectslog odds ratiolog risk ratiometa-regressionrobust standard errors

More Related Videos

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

1.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Related Experiment Videos

Last Updated: Apr 11, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.1K
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

1.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
  • The study examined various parameter values across different effect size metrics.
  • Accuracy of confidence intervals was assessed, with results reported for intercept and slope estimations.

    • Main Results:

    • The robust variance estimator demonstrated good performance even with as few as 10 studies.
    • Coverage was generally less than nominal for slope estimations compared to intercept estimations.
    • The method proved reliable for common effect sizes like risk difference, log risk ratio, and log odds ratio.

    Conclusions:

    • The robust variance estimation method is a viable approach for meta-analyses with dependent effect sizes, even in small sample scenarios.
    • While generally accurate, users should be mindful of potentially lower coverage in slope estimation.
    • The findings support the application of this method in fields like cognitive behavior therapy meta-analyses.