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

Odds Ratio01:09

Odds Ratio

235
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
235
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

84
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...
84
Weighted Mean00:57

Weighted Mean

5.3K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.3K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

507
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
507
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

165
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
165
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.3K

You might also read

Related Articles

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

Sort by
Same author

Defining Success and Failure In Prosthetic Joint Infections: A Meta-epidemiologic Study Toward A Core Outcome Set.

Open forum infectious diseases·2026
Same author

A systematic review supporting the Endocrine Society clinical practice guidelines on central precocious puberty.

The Journal of clinical endocrinology and metabolism·2026
Same author

Evaluating data extraction error by a large language model from randomised controlled trials: a large-scale empirical study.

BMJ evidence-based medicine·2026
Same author

A novel visualization approach for network meta-analysis: The plate plot and the nmaplateplot R package.

Research synthesis methods·2026
Same author

Investigating the impact of problematic evidence on clinical practice guidelines and associated patient outcomes (VITALITY Study II): protocol.

BMJ open·2026
Same author

Evidence contamination: what it is and why it matters.

BMJ evidence-based medicine·2026

Related Experiment Video

Updated: Sep 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

687

A Bayesian model for combining standardized mean differences and odds ratios in the same meta-analysis.

Yaqi Jing1,2, Mohammad Hassan Murad3, Lifeng Lin1,4

  • 1Department of Statistics, Florida State University, Tallahassee, Florida, USA.

Journal of Biopharmaceutical Statistics
|August 3, 2022
PubMed
Summary

A new Bayesian model synthesizes continuous and binary study data for meta-analysis, outperforming traditional conversion methods. This approach offers more accurate results by directly modeling different outcome types, improving meta-analysis reliability.

Keywords:
Bayesian hierarchical modelbinary and continuous outcomesmeta-analysisodds ratiostandardized mean difference

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
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K

Related Experiment Videos

Last Updated: Sep 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

687
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
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K

Area of Science:

  • Biostatistics
  • Medical Informatics
  • Epidemiology

Background:

  • Meta-analysis often combines studies with continuous (e.g., depression scores) and binary (e.g., depression counts) outcomes.
  • Existing methods use a conversion approach assuming logistic distributions, which can be inaccurate for large effect sizes or rare events.

Approach:

  • Proposes a Bayesian hierarchical model to directly synthesize standardized mean differences (SMDs) and odds ratios (ORs).
  • Employs exact likelihoods for continuous and binary outcomes, accounting for full uncertainty.
  • Compares the Bayesian method against the conventional conversion method via simulation studies.

Key Points:

  • The Bayesian method generally yields less biased results with lower mean squared errors and higher coverage probabilities.
  • Superior performance of the Bayesian method is contingent on the normality assumption for continuous data.
  • The Bayesian approach can introduce bias with non-normal continuous data.

Conclusions:

  • The proposed Bayesian model offers a more accurate alternative to conventional conversion methods for meta-analyzing mixed continuous and binary outcomes.
  • Demonstrates the utility of the Bayesian method through two real-world case studies.
  • Highlights the importance of data distribution assumptions in meta-analysis methodology.