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

Censoring Survival Data

Censoring Survival Data

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...

Assumptions of Survival Analysis

Assumptions of Survival Analysis

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.

Hindsight Biases

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?

Causality in Epidemiology

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...

Cause and Effect

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?

Kaplan-Meier Approach

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

You might also read

Related Articles

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

Sort by

Same author

Estimating Effects of Longitudinal Modified Treatment Policies (LMTPs) on Rates of Change in Health Outcomes.

Statistics in medicine·2026

Same author

Re: Generalizing and Transporting Causal Inferences from Randomized Trials in the Presence of Trial Engagement Effects.

Epidemiology (Cambridge, Mass.)·2026

Same author

Accelerating real-world prediction and research in Alzheimer's: The M3AD study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same author

Health Equity Perspective on Data-Driven Treatment Decisions in Cardiovascular Care: Risk Assessments Versus Individualized Treatment Rules.

Journal of the American Heart Association·2026

Same author

Discussion on 'Causal inference with misspecified network interference structure' by Bar Weinstein and Daniel Nevo.

Biometrics·2026

Same author

Post-selection inference for causal effects after causal discovery.

Biometrika·2026

Search research articles

Related Experiment Video

Updated: May 22, 2025

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Published on: April 18, 2017

Causal Inference With Outcome-Dependent Missingness And Self-Censoring.

Jacob M Chen¹, Daniel Malinsky², Rohit Bhattacharya¹

¹Department of Computer Science, Williams College.

Proceedings of Machine Learning Research

|March 14, 2025

Summary

This summary is machine-generated.

This study introduces a new test to address biased causal effect estimates caused by self-censoring outcomes. The method uses a randomized incentive to validate assumptions, enabling accurate causal inference even with missing data.

More Related Videos

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: May 22, 2025

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Published on: April 18, 2017

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Causal Inference
Biostatistics
Missing Data Methods

Background:

Missing outcomes can bias causal effect estimates, particularly when outcomes influence their own missingness (self-censoring).
Existing methods like the shadow variable approach have challenges in verifying their underlying assumptions.
Confounding bias further complicates accurate estimation in observational studies.

Purpose of the Study:

To develop a test for verifying identification assumptions in causal inference with self-censoring outcomes.
To provide a method for correcting both self-censoring and confounding bias.
To introduce an intuitive inverse probability weighting estimator for causal effects.

Main Methods:

Proposed a test utilizing a randomized incentive variable to encourage outcome reporting.
The test verifies if pre-treatment covariates block backdoor paths between treatment-outcome and treatment-missingness indicator.
Developed an inverse probability weighting estimator using treatment and response weights.

Main Results:

Demonstrated that the proposed test can verify assumptions sufficient for bias correction.
Showed that the treatment can serve as a shadow variable under verified conditions.
The inverse probability weighting estimator was shown to be effective in simulations.

Conclusions:

The new testing framework effectively addresses bias from self-censoring and confounding in causal inference.
The randomized incentive approach offers a practical way to validate necessary assumptions.
The study provides a robust method for identifying causal effects with missing outcomes.