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

Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.8K
Causality in Epidemiology01:21

Causality in Epidemiology

1.5K
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...
1.5K
Relative Risk01:12

Relative Risk

1.8K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
1.8K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

388
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.
388
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

387
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
387
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.6K

You might also read

Related Articles

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

Sort by
Same author

The Impact of Transplant Waitlisting Measures on Dialysis Facilities' Star Ratings.

Health services research·2025
Same author

What's the Weight? Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research.

Statistics in medicine·2025
Same author

Adding New Components to a Composite Quality Metric: How Good Is Good Enough?

Medical care·2025
Same author

ipd: an R package for conducting inference on predicted data.

Bioinformatics (Oxford, England)·2025
Same author

Early Acute Kidney Injury in Adult Patients With Burns in Australia & New Zealand.

The Journal of surgical research·2024
Same author

Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned.

Harvard data science review·2024

Related Experiment Video

Updated: Jan 10, 2026

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

2.5K

A Pseudo-Value Approach to Causal Deep Learning of Semi-Competing Risks.

Stephen Salerno1, Yi Li2

  • 1Public Health Science Division, Biostatistics Fred Hutchinson Cancer Center Seattle, WA.

Arabian Journal of Mathematics
|November 21, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning method to accurately estimate cancer treatment effects on non-fatal outcomes like recurrence, even with competing risks. The approach improves causal inference for personalized lung cancer care.

More Related Videos

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

10.7K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.4K

Related Experiment Videos

Last Updated: Jan 10, 2026

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

2.5K
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

10.7K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.4K

Area of Science:

  • Biostatistics
  • Machine Learning in Medicine
  • Cancer Research

Background:

  • Cancer studies often prioritize mortality, overlooking non-fatal events like disease recurrence.
  • Recurrence is a critical endpoint in lung cancer, influencing treatment options and patient care.
  • Causal inference for non-fatal outcomes is complicated by semi-competing risks, where death can prevent recurrence.

Purpose of the Study:

  • To develop a robust deep learning approach for estimating the causal effect of treatments on non-fatal cancer outcomes.
  • To address challenges in causal inference posed by dependent censoring and complex covariate relationships in semi-competing risks.
  • To accurately estimate survival average causal effects for personalized cancer treatment strategies.

Main Methods:

  • A three-stage deep learning framework combining Archimedean copula for survival functions and a jackknife pseudo-value approach.
  • Estimation of pseudo-survival probabilities at fixed time points to serve as target values for causal estimators.
  • Utilization of a deep neural network to link pseudo-outcomes, causal variables, and confounders for direct standardization.

Main Results:

  • The proposed method provides consistent causal estimators without requiring proportional hazards assumptions.
  • Numerical studies demonstrated the approach's effectiveness in handling dependent censoring and complex confounders.
  • Application to the Boston Lung Cancer Study provided insights into the causal effect of surgical resection on recurrence.

Conclusions:

  • The deep learning approach offers a powerful tool for causal inference in cancer research, particularly for non-fatal endpoints.
  • This method enhances the ability to assess treatment effects on disease recurrence, improving personalized cancer care.
  • The study highlights the potential of advanced machine learning techniques to overcome limitations in traditional survival analysis for complex clinical data.