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

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...
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
Hazard Ratio01:12

Hazard Ratio

The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial evaluating a...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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 from...
Weighted Mean00:57

Weighted Mean

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...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

You might also read

Related Articles

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

Sort by
Same author

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same author

Finding distributions that differ, with false discovery rate control.

Biometrika·2026
Same author

Identification and Estimation of Vaccine Effectiveness in the Test-Negative Design Under Equi-confounding.

Epidemiology (Cambridge, Mass.)·2025
Same author

Regression-based Proximal Causal Inference for Right-censored Time-to-event Data.

Epidemiology (Cambridge, Mass.)·2025
Same author

Real-world effectiveness and causal mediation study of BNT162b2 on long COVID risks in children and adolescents.

EClinicalMedicine·2024
Same author

Regression-Based Proximal Causal Inference.

American journal of epidemiology·2024

Related Experiment Video

Updated: May 10, 2026

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

Inverse odds ratio-weighted estimation for causal mediation analysis.

Eric J Tchetgen Tchetgen1

  • 1Department of Epidemiology, Harvard University, Boston, MA, U.S.A.; Department of Biostatistics, Harvard University, Boston, MA, U.S.A.

Statistics in Medicine
|June 8, 2013
PubMed
Summary

This study introduces a new inverse odds ratio-weighted method for mediation analysis. This approach effectively estimates natural direct and indirect effects in various regression models, accommodating multiple mediators.

Keywords:
causal mediation analysisdouble robustnessinverse odds ratio-weighted estimationnatural direct and indirect effects

More Related Videos

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Related Experiment Videos

Last Updated: May 10, 2026

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

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Area of Science:

  • Health and social sciences
  • Biostatistics
  • Epidemiology
  • Causal inference

Background:

  • Quantifying mediation is crucial for understanding causal pathways in health and social sciences.
  • Existing mediation analysis frameworks offer new definitions and estimators for direct and indirect effects.
  • A need exists for versatile methods to decompose total effects in diverse statistical models.

Purpose of the Study:

  • To present a novel inverse odds ratio-weighted approach for estimating natural direct and indirect effects.
  • To demonstrate the universality of this method across various regression models.
  • To facilitate mediation analysis with multiple mediators of different data types.

Main Methods:

  • Introduced an inverse odds ratio-weighted estimation strategy.
  • The method utilizes weights derived from the odds ratio function between exposure and mediator.
  • Applicable to generalized linear models, Cox proportional hazards regression, and other standard models.

Main Results:

  • The proposed method provides a universal approach for effect decomposition in common regression settings.
  • It enables the estimation of natural direct and indirect effects.
  • The approach readily incorporates categorical, discrete, or continuous multiple mediators.

Conclusions:

  • The inverse odds ratio-weighted method offers a simple and implementable solution for mediation analysis.
  • Its versatility makes it suitable for a wide range of statistical models and mediator types.
  • This facilitates a more comprehensive understanding of causal pathways in research.