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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

321
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...
321
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

474
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
474
Methods of Medium Optimization01:28

Methods of Medium Optimization

45
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
45
Structuralism01:26

Structuralism

4.0K
Structuralism, an early psychological theory developed by Wilhelm Wundt and his student Edward Bradford Titchener, sought to dissect the human mind into its most fundamental components. Wundt's groundbreaking work in his laboratory set the stage for Titchener to define structuralism's goal as cataloging the "atoms" of the mind—sensations, images, and feelings—akin to how chemists identify elements of matter.
Titchener's approach to structuralism was unique. He...
4.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

397
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
397
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

3.0K
A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
3.0K

You might also read

Related Articles

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

Sort by
Same author

Quantifying treatment-related travel burden and its association with mortality in pediatric cancer: An analysis of state cancer registry data.

Cancer epidemiology·2026
Same author

Polarisome core component FgPea2 regulates FgBoi2-mediated polarized growth, pathogenicity and environmental stress in Fusarium graminearum.

Stress biology·2026
Same author

Genetic diversity analysis and core collection construction of Macadamia spp. from Yunnan using fourfold degenerate SNP sites.

BMC plant biology·2026
Same author

Case Report: Extracorporeal membrane oxygenation in acute coronary syndrome: a rare case of massive left ventricular thrombus.

Frontiers in cardiovascular medicine·2026
Same author

Exploring the Differences in Alkaloids of Fritillariae Thunbergii Bulbus From Different Regions Using Liquid Chromatography-Mass Spectrometry and Chemometrics.

Chemistry & biodiversity·2026
Same author

A comprehensive narrative review of <i>Epimedium</i> and its bioactive compounds in respiratory diseases.

Journal of pharmaceutical analysis·2026

Related Experiment Video

Updated: Mar 28, 2026

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

7.3K

Causal Models for Mediation Analysis: An Introduction to Structural Mean Models.

Cheng Zheng1, David C Atkins2, Xiao-Hua Zhou3

  • 1a Zilber School of Public Health , University of Wisconsin-Milwaukee.

Multivariate Behavioral Research
|December 31, 2015
PubMed
Summary

Causal mediation analysis helps understand intervention effects. A rank-preserving model (RPM) offers a robust alternative to traditional methods, especially when unmeasured confounders are present, providing less biased results.

Keywords:
clinical trialsmediation analysisstructural mean models

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

4.5K

Related Experiment Videos

Last Updated: Mar 28, 2026

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

7.3K
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

4.5K

Area of Science:

  • Statistics
  • Behavioral Science
  • Epidemiology

Background:

  • Mediation analyses are crucial for understanding the mechanisms of behavioral interventions.
  • Traditional causal mediation approaches rely on the strong assumption of sequential ignorability.
  • Unmeasured confounding can bias traditional mediation effect estimates.

Purpose of the Study:

  • To introduce and evaluate structural mean models (SMMs) as an alternative to traditional causal mediation analysis.
  • To present a specific SMM, the rank-preserving model (RPM), and discuss its assumptions and application.
  • To compare the performance of RPM with traditional mediation approaches under various conditions, including unmeasured confounding.

Main Methods:

  • The study introduces structural mean models (SMMs), specifically the rank-preserving model (RPM).
  • The rank-preserving model (RPM) is applied to college student drinking data for an empirical comparison.
  • Simulations are used to assess the bias of traditional mediation and RPM under different scenarios, including unmeasured confounding.

Main Results:

  • Applying traditional and RPM mediation approaches to college student drinking data yielded different effect magnitudes.
  • Simulated data showed that traditional mediation can produce biased results when unmeasured confounders are present.
  • The RPM approach demonstrated unbiased results in simulated scenarios where traditional methods failed, provided its own assumptions were met.

Conclusions:

  • The rank-preserving model (RPM) offers a valuable alternative for causal mediation analysis, particularly when unmeasured confounders are suspected.
  • RPM requires specific assumptions, including a strong moderator of the intervention-mediator relationship and no confounding of the intervention-mediator or mediator-outcome paths.
  • When its assumptions are met, RPM provides a more robust causal interpretation of mediation compared to traditional methods in the presence of unmeasured confounding.