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

Relative Risk01:12

Relative Risk

2.5K
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...
2.5K
Survival Tree01:19

Survival Tree

499
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
499
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

712
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...
712
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

852
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
852
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

492
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.
492
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

955
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...
955

You might also read

Related Articles

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

Sort by
Same author

A precision randomized trial of hepatitis C treatment support among people who inject drugs in India: The STOP-C Trial.

Journal of hepatology·2026
Same author

Super greedy trees.

Artificial intelligence review·2026
Same author

Variable Priority for Unsupervised Variable Selection.

Pattern recognition·2026
Same author

Individual variable priority: a model-independent local gradient method for variable importance.

Artificial intelligence review·2025
Same author

Harnessing the power of virtual (digital) twins: Graphical causal tools for understanding patient and hospital differences.

Computational and structural biotechnology journal·2025
Same author

Evaluating allograft risk models in organ transplantation: Understanding and balancing model discrimination and calibration.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society·2025

Related Experiment Video

Updated: May 1, 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

2.3K

Random survival forests for competing risks.

Hemant Ishwaran1, Thomas A Gerds2, Udaya B Kogalur3

  • 1Division of Biostatistics, University of Miami, Miami, FL 33136, USA hemant.ishwaran@gmail.com.

Biostatistics (Oxford, England)
|April 15, 2014
PubMed
Summary
This summary is machine-generated.

We present a novel non-parametric random forest approach for competing risks analysis. This method effectively predicts outcomes and selects relevant variables, even in complex, high-dimensional datasets like HIV/AIDS research.

Keywords:
AIDSBrier scoreC-indexCompeting risksCumulative incidence functionEnsemble

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

9.9K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

1.0K

Related Experiment Videos

Last Updated: May 1, 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

2.3K
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

9.9K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

1.0K

Area of Science:

  • Biostatistics
  • Machine Learning
  • Epidemiology

Background:

  • Competing risks analysis is crucial in medical research, particularly for diseases with multiple potential outcomes.
  • Traditional methods may struggle with high-dimensional data and identifying event-specific predictors.

Purpose of the Study:

  • To introduce a novel, non-parametric random forest methodology for competing risks.
  • To enable both accurate prediction of cumulative incidence and selection of event-specific variables.

Main Methods:

  • Utilizing random forests for a fully non-parametric approach to competing risks.
  • Applying the method to high-dimensional datasets and scenarios with multiple competing events.

Main Results:

  • Demonstrated high effectiveness in prediction tasks.
  • Showcased significant utility in variable selection for complex problems.
  • Validated performance in challenging settings like HIV/AIDS studies.

Conclusions:

  • The proposed random forest method offers a powerful, non-parametric tool for competing risks.
  • It is highly effective for prediction and variable selection in high-dimensional and multi-event scenarios.