Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

Confounding in Epidemiological Studies

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...

Friedman Two-way Analysis of Variance by Ranks

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...

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...

You might also read

Related Articles

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

Sort by

Same author

Developmental Disorders in Children Recently Diagnosed With Cancer.

Pediatric blood & cancer·2026

Same author

Association of Medicaid expansion with colon cancer care: treatment patterns and survival in non-metastatic cases from state registry-claims data.

Cancer causes & control : CCC·2025

Same author

Maternal Childhood Trauma and Children's Developmental Course of Aggressive Behavior from Ages 4 to 12.

Psychology of violence·2025

Same author

A Rising Tide Raises All Ships: Was the Effect of Medicaid Expansion on Cancer Outcomes Similar Across Subgroups of Patients With Cancer on Medicaid?

AJPM focus·2025

Same author

Validity and reliability of Brier scoring for assessment of probabilistic diagnostic reasoning.

Diagnosis (Berlin, Germany)·2024

Same author

Multilevel Interventions and Dental Attendance in Pediatric Primary Care: A Cluster Randomized Clinical Trial.

JAMA network open·2024

Search research articles

Related Experiment Videos

Distribution-free mediation analysis for nonlinear models with confounding.

Jeffrey M Albert¹

¹Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH 44120, USA. jma13@case.edu

Epidemiology (Cambridge, Mass.)

|September 26, 2012

Summary

This summary is machine-generated.

This study introduces a new causal mediation analysis method using empirical distributions, improving direct and indirect effect estimation without parametric assumptions. The novel approach shows lower bias compared to traditional mediation formulas, especially when mediator distributions are complex.

Related Experiment Videos

Area of Science:

Epidemiology
Biostatistics
Causal Inference

Background:

Causal mediation analysis estimates direct and indirect effects of exposures on outcomes.
Traditional methods rely on parametric assumptions for mediator distributions, which can be limiting and difficult to specify accurately.
Existing approaches may introduce bias if mediator distributions are misspecified.

Purpose of the Study:

To propose a novel, non-parametric method for causal mediation analysis.
To estimate natural direct and indirect effects without assuming a specific mediator distribution.
To address limitations of the standard parametric mediation formula.

Main Methods:

Developed a new causal mediation analysis method utilizing the empirical distribution function.
Incorporated inverse-probability weighting to adjust for confounders in exposure-mediator and exposure-outcome relationships.
Applied the method to a cohort study of dental caries in very-low-birth-weight adolescents, examining the oral-hygiene index as a mediator.

Main Results:

Simulation studies demonstrated low bias in estimating direct and indirect effects across various distribution scenarios.
The proposed empirical distribution function method outperformed the standard mediation formula when mediator distributions were misspecified.
The method successfully estimated effects in a real-world cohort study.

Conclusions:

The proposed non-parametric method offers a robust alternative for causal mediation analysis.
This approach enhances the reliability of direct and indirect effect estimation by avoiding restrictive parametric assumptions.
The method is valuable for complex epidemiological and public health research involving mediation.