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

182
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...
182
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

302
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
302
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

897
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
897
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

280
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...
280
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

621
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
621
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

416
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
416

You might also read

Related Articles

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

Sort by
Same author

Caveats on Using Firth's Penalization in the Model-Based Regression Standardization for Rare Diseases.

Statistics in medicine·2026
Same author

Effect of home visiting support on maternal psychosocial needs and postnatal depression: emulating a target trial.

BMJ mental health·2026
Same author

Beyond the Hazard Ratio: Causal Inference from Time-to-Event Data with Dependent Censoring, Confounding, and Competing Risks.

Journal of epidemiology·2026
Same author

Prognostic Impact of Renal Function on Outcomes After Physiology-Guided Coronary Revascularization: Insights From the J-PRIDE Registry.

Circulation. Cardiovascular interventions·2026
Same author

Prognostic Implications of Bleeding and Ischemic Complications in Acute Myocardial Infarction-Related Cardiogenic Shock Managed With Microaxial Flow Pump.

Circulation. Cardiovascular interventions·2026
Same author

Dynamic Borrowing With a Bias-Tolerance Cap in Augmented Randomized Controlled Trials.

Statistics in medicine·2026

Related Experiment Video

Updated: Dec 14, 2025

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

6.7K

Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips.

Tomohiro Shinozaki1, Etsuji Suzuki2

  • 1Department of Information and Computer Technology, Faculty of Engineering, Tokyo University of Science.

Journal of Epidemiology
|July 21, 2020
PubMed
Summary

Estimating effects of time-varying exposures with complex longitudinal data requires advanced statistical methods. This study clarifies marginal structural models and inverse probability weighting for accurate causal effect estimation.

Keywords:
causal inferenceg-formulainverse probability weightingmarginal structural modeltime-varying exposure

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

3.6K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K

Related Experiment Videos

Last Updated: Dec 14, 2025

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

6.7K
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

3.6K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Longitudinal studies frequently involve time-varying exposures and confounders.
  • Prior exposures can influence subsequent confounders, complicating effect estimation.
  • Standard methods may be insufficient when exposures and confounders are dynamic.

Purpose of the Study:

  • To clarify statistical techniques for estimating effects of time-varying exposures in complex longitudinal data.
  • To distinguish marginal structural models from inverse probability weighting methods.
  • To provide practical guidance on specifying and implementing these models.

Main Methods:

  • Illustrates generalized g-formula (standardization) and inverse probability weighting.
  • Demonstrates specification of marginal structural models for time-varying exposures.
  • Utilizes a novel hypothetical example with accessible potential outcomes.

Main Results:

  • Highlights common misunderstandings regarding marginal structural models and inverse probability weighting.
  • Provides a step-by-step illustration of estimation techniques.
  • Offers practical tips for concrete understanding of complex exposure effect estimation.

Conclusions:

  • Accurate estimation of time-varying exposure effects necessitates careful model specification.
  • Distinguishing causal models from nuisance models is crucial for valid inference.
  • The provided methods and illustrations aid epidemiologists in handling complex longitudinal data.