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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

398
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...
398
Two-Way ANOVA01:17

Two-Way ANOVA

3.1K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
3.1K
One-Way ANOVA01:18

One-Way ANOVA

10.7K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
10.7K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.4K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.4K
Regression Analysis01:11

Regression Analysis

7.2K
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:
7.2K
Factorial Design02:01

Factorial Design

13.5K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.5K

You might also read

Related Articles

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

Sort by
Same author

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same author

The interplay between social connection and compliance with COVID-19 preventive measures.

European journal of public health·2026
Same author

Analyzing multiple mediators in multiple single-mediator models leads to wrong conclusions.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

The mediating role of recreational physical activity and dietary behavior in the relationship between family affluence and mental well-being: an interventional effects approach.

Journal of behavioral medicine·2025
Same author

Investigating lived ostracism: valid causal inference requires articulating the causal estimand.

The Journal of social psychology·2025
Same author

Causal Machine Learning Methods and Use of Cross-Fitting in Settings With High-Dimensional Confounding.

Statistics in medicine·2025
Same journal

Statistical analysis of disease onset during lifespan with left truncation.

Biometrics·2026
Same journal

Interim analysis in sequential multiple assignment randomized trials for survival outcomes.

Biometrics·2026
Same journal

Acknowledgment of Referees 2025.

Biometrics·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Nov 29, 2025

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

4.2K

Nonlinear mediation analysis with high-dimensional mediators whose causal structure is unknown.

Wen Wei Loh1, Beatrijs Moerkerke1, Tom Loeys1

  • 1Department of Data Analysis, Ghent University, Gent, Belgium.

Biometrics
|November 20, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to understand how multiple biological mediators affect patient outcomes, simplifying complex causal pathways for better analysis of gene expression and cancer mortality.

Keywords:
collapsibilitydirect and indirect effectseffect decompositionmarginal and conditional effectsmultiple mediation analysispath analysis

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

7.1K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.6K

Related Experiment Videos

Last Updated: Nov 29, 2025

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

4.2K
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

7.1K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.6K

Area of Science:

  • Causal inference
  • Biostatistics
  • Genomics

Background:

  • Decomposing treatment effects through multiple mediators is complex.
  • Existing methods require strong assumptions and modeling of joint mediator distributions.
  • High-dimensional and mixed-type mediators pose significant challenges.

Purpose of the Study:

  • To develop a novel estimation strategy for interventional direct and indirect effects with multiple mediators.
  • To avoid modeling the joint distribution of mediators, accommodating high-dimensional and mixed-type mediators.
  • To provide scientifically relevant causal interpretations under weaker conditions.

Main Methods:

  • Utilizing a definition of interventional effects for longitudinal mediation.
  • Employing nonparametric estimates of counterfactual mediator distributions.
  • Accommodating noncontinuous outcomes with nonlinear models and using Monte Carlo integration for estimation.

Main Results:

  • The proposed method successfully estimates interventional effects without specifying the joint mediator distribution.
  • Demonstrated applicability using genomic data to analyze microRNA effects on brain cancer mortality.
  • The approach handles complex mediator structures and mixed data types effectively.

Conclusions:

  • The novel estimation strategy offers a more flexible and robust approach to mediation analysis with multiple mediators.
  • This method simplifies the analysis of complex biological pathways, particularly in high-dimensional genomic data.
  • Facilitates a deeper understanding of treatment effects and biological mechanisms in diseases like brain cancer.