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

Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

155
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...
155
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

261
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...
261
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
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...
86
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

196
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.
196
Longitudinal Studies01:26

Longitudinal Studies

238
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
238
Censoring Survival Data01:09

Censoring Survival Data

230
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
230

You might also read

Related Articles

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

Sort by
Same author

On semi-supervised estimation using exponential tilt mixture models.

Journal of statistical planning and inference·2025
Same author

Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2024
Same author

Consistent and robust inference in hazard probability and odds models with discrete-time survival data.

Lifetime data analysis·2022
Same author

Discussion on "Instrumented difference-in-differences" by Ye, Ertefaie, Flory, Hennessy, Small.

Biometrics·2022

Related Experiment Video

Updated: Sep 11, 2025

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

3.4K

Sensitivity models and bounds under sequential unmeasured confounding in longitudinal studies.

Zhiqiang Tan1

  • 1Department of Statistics, Rutgers University, 110 Frelinghuysen Road, Piscataway, New Jersey 08854, U.S.A.

Biometrika
|August 18, 2025
PubMed
Summary

This study introduces new methods for causal inference sensitivity analysis in longitudinal studies. It assesses potential impacts of unmeasured confounding on treatment effects and outcomes.

Keywords:
Marginal sensitivity modelSensitivity analysisSequential non-confoundingSequential unmeasured confoundingTime-varying treatment

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
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

6.4K

Related Experiment Videos

Last Updated: Sep 11, 2025

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

3.4K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
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

6.4K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Causal inference in longitudinal studies with time-varying treatments and covariates presents challenges.
  • Assessing the impact of unmeasured confounding is crucial for reliable study findings.

Purpose of the Study:

  • To develop and evaluate multi-period sensitivity models for causal inference.
  • To quantify worst-case bounds for counterfactual outcomes and average treatment effects under unmeasured confounding.

Main Methods:

  • Formulation of several multi-period sensitivity models (primary, joint, product).
  • Relaxation of the assumption of sequential non-confounding.
  • Establishment of explicit population-level bounds using convex optimization on observed data.

Main Results:

  • Developed novel sensitivity models for longitudinal causal inference.
  • Provided explicit representations for sharp and conservative bounds.
  • Demonstrated a generalization from cross-sectional marginal sensitivity models.

Conclusions:

  • The proposed multi-period sensitivity models offer a robust framework for analyzing longitudinal data.
  • These methods allow for a more comprehensive assessment of potential bias due to unmeasured confounding.
  • The findings advance the field of causal inference by extending sensitivity analysis to complex longitudinal settings.