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

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

423
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
423
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

5.3K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
5.3K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.4K
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...
3.4K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

173
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
173
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

264
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...
264
Test for Homogeneity01:23

Test for Homogeneity

2.0K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Availability of mechanical circulatory support (MCS) and hospital survival in ST-segment elevation myocardial infarction related cardiogenic shock (STEMI-CS).

European heart journal. Acute cardiovascular care·2026
Same author

Stem-Cell-Derived Biologic Ventricular Assist Tissue in Heart Failure.

The New England journal of medicine·2026
Same author

Effect of anti-fibrotic therapy on regression of myocardial fibrosis after TAVI: design and rationale of the Reduce-MFA DZHK25 trial.

ESC heart failure·2026
Same author

The microRNA inhibitor CDR132L in patients with reduced left ventricular ejection fraction after myocardial infarction: a randomized phase 2 trial.

Nature medicine·2026
Same author

Autologous haematopoietic stem cell transplantation in multiple sclerosis: outcomes and predictors from German real-world data.

Journal of neurology, neurosurgery, and psychiatry·2026
Same author

Bayesian Random-Effects Meta-Analysis of Aggregate Data on Clinical Events.

Statistics in medicine·2026

Related Experiment Video

Updated: Aug 4, 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

630

Summarizing empirical information on between-study heterogeneity for Bayesian random-effects meta-analysis.

Christian Röver1, Sibylle Sturtz2, Jona Lilienthal2

  • 1Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.

Statistics in Medicine
|April 2, 2023
PubMed
Summary
This summary is machine-generated.

Bayesian meta-analysis requires prior probabilities for heterogeneity, especially with few studies. This study extends the normal-normal hierarchical model to infer heterogeneity priors from empirical data, offering practical approaches for distribution fitting.

Keywords:
external informationheterogeneityhierarchical modelmeta-analysisprior distribution

More Related Videos

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

3.4K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.0K

Related Experiment Videos

Last Updated: Aug 4, 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

630
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

3.4K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.0K

Area of Science:

  • Biostatistics
  • Statistical Modeling

Background:

  • Bayesian meta-analysis frequently necessitates prior probability specifications for between-study heterogeneity.
  • This is particularly crucial in meta-analyses involving a limited number of studies.
  • Existing methods for utilizing historical data to inform these priors are often inadequate.

Approach:

  • The study extends the standard normal-normal hierarchical model for random-effects meta-analysis.
  • It introduces a method to infer a heterogeneity prior directly from empirical data.
  • Focuses on simple, applicable strategies for fitting parametric distributions to observed heterogeneity data.

Key Points:

  • Demonstrates fitting a distribution to empirically observed heterogeneity data from multiple meta-analyses.
  • Highlights the importance of selecting an appropriate parametric distribution family.
  • Provides practical guidance for translating empirical findings into prior probability distributions.

Conclusions:

  • The proposed extension offers a more robust method for specifying priors in Bayesian meta-analysis.
  • This approach enhances the reliability of meta-analysis results, especially in data-scarce scenarios.
  • Facilitates the informed use of historical data for setting heterogeneity priors.