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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

222
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
222
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

154
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.
154
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

277
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...
277
Censoring Survival Data01:09

Censoring Survival Data

129
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...
129
Actuarial Approach01:20

Actuarial Approach

96
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
96
Cancer Survival Analysis01:21

Cancer Survival Analysis

383
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
383

You might also read

Related Articles

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

Sort by
Same author

Household Transmission of Enterovirus D68, Washington and Oregon, United States, 2022-2024.

Emerging infectious diseases·2026
Same author

Targeting macrophage ferritin heavy chain mitigates ferroptosis and lung injury in experimental acute respiratory distress syndrome.

Nature communications·2026
Same author

Comparing causal parameters with many treatments and positivity violations.

Biometrika·2026
Same author

Influenza household transmission and genomic diversity in the United States: A prospective cohort study, 2022-2024.

The Journal of infection·2026
Same author

Correlates of severe and delta COVID-19 in a phase 3 trial of the AZD1222 vaccine.

NPJ vaccines·2026
Same author

Recanting Twins: Addressing Intermediate Confounding in Mediation Analysis.

Statistics in medicine·2026

Related Experiment Video

Updated: Jul 18, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.2K

Causal survival analysis under competing risks using longitudinal modified treatment policies.

Iván Díaz1, Katherine L Hoffman2, Nima S Hejazi3

  • 1Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, 10016, USA. ivan.diaz@nyu.edu.

Lifetime Data Analysis
|August 24, 2023
PubMed
Summary

This study introduces a new method for causal inference in longitudinal studies, extending longitudinal modified treatment policies (LMTP) to handle time-to-event outcomes with competing risks. The approach improves causal effect estimation in complex health scenarios.

Keywords:
Competing risksDouble machine learningModified treatment policiesTargeted minimum loss-based estimation

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

2.1K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

Related Experiment Videos

Last Updated: Jul 18, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.2K
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

2.1K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

Area of Science:

  • Causal inference
  • Biostatistics
  • Epidemiology

Background:

  • Longitudinal modified treatment policies (LMTP) offer a novel approach to define and estimate causal parameters related to treatment.
  • LMTPs are crucial for analyzing longitudinal studies with multiple treatments over time.
  • Existing methods may not adequately address time-to-event outcomes complicated by competing events.

Purpose of the Study:

  • To extend the LMTP methodology to time-to-event outcomes with competing events.
  • To develop non-parametric, locally efficient estimators for causal effects in such settings.
  • To apply the extended LMTP to analyze the impact of time-to-intubation on acute kidney injury in COVID-19 patients.

Main Methods:

  • Extension of LMTP methodology for competing risks.
  • Development of identification results and non-parametric locally efficient estimators.
  • Utilizing flexible, data-adaptive regression techniques to minimize model misspecification bias.
  • Ensuring estimators retain important asymptotic properties like [Formula: see text]-consistency.

Main Results:

  • The study provides a framework for identifying and estimating causal effects in longitudinal studies with competing risks.
  • Data-adaptive methods are employed to enhance the robustness of estimators against model misspecification.
  • The methodology is demonstrated through an application to COVID-19 patient data.

Conclusions:

  • The extended LMTP methodology effectively addresses complex causal inference problems with time-to-event data and competing risks.
  • The proposed estimators offer improved accuracy and robustness in real-world applications.
  • This advancement has significant implications for epidemiological and clinical research, particularly in critical care settings.