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

658
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...
658
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

460
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.
460
Survival Tree01:19

Survival Tree

447
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...
447
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

658
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
658
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

878
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...
878
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Lavandin cell extract enriched in rosmarinic acid attenuates inflammation via AMPK/Nrf2/HO-1 activation and IKK/IκB/NF-κB inhibition.

BMC plant biology·2026
Same author

Avoid All the Competitive Ones: Dynamics of Altruistic Behavior, Mediators, and Moderators in an Evacuation Drill.

Behavioral sciences (Basel, Switzerland)·2026
Same author

Comparison of Fludarabine Versus Bendamustine as a Lymphodepleting Chemotherapy Prior to CAR-T for Large Cell Lymphoma.

Transplantation and cellular therapy·2026
Same author

A ratiometric fluorescent reporter of mitochondrial sodium.

Nature chemical biology·2026
Same author

STAT6 inhibition of M2 macrophages suppresses tumor growth by modulating the tumor microenvironment in colon cancer model.

Frontiers in immunology·2026
Same author

Differential Myelin and Axon-Dependent Recovery Based on Symptom Duration in Degenerative Cervical Myelopathy.

Neurosurgery·2026
Same journal

Shared frailty sieve estimation for dependent left truncated and interval censored data.

Lifetime data analysis·2026
Same journal

Functional win-fractions regression models for composite outcomes.

Lifetime data analysis·2026
Same journal

Variable selection in causal semiparametric transformation models with all-or-nothing treatment compliance.

Lifetime data analysis·2026
Same journal

Correction to: A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model.

Lifetime data analysis·2026
Same journal

Unobserved heterogeneity in threshold regression based on the hitting times of a reflected Brownian motion for recurrent hypoglycemia.

Lifetime data analysis·2026
Same journal

Variable selection with broken adaptive ridge regression for interval-censored competing risks data.

Lifetime data analysis·2026
See all related articles

Related Experiment Video

Updated: Feb 25, 2026

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

10.9K

Group and within-group variable selection for competing risks data.

Kwang Woo Ahn1, Anjishnu Banerjee2, Natasha Sahr2

  • 1Division of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA. kwooahn@mcw.edu.

Lifetime Data Analysis
|August 6, 2017
PubMed
Summary
This summary is machine-generated.

We introduce a new adaptive group bridge method for selecting important variables in competing risks data, simultaneously identifying key groups and individual variables within them. This approach improves variable selection consistency and outperforms existing methods in simulations and real-world applications.

Keywords:
Adaptive penaltyClustered dataCompeting risks dataGroup bridge

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

2.7K
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

953

Related Experiment Videos

Last Updated: Feb 25, 2026

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

10.9K
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

2.7K
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

953

Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Variable selection is challenging for competing risks data, especially with grouped variables.
  • Existing methods often focus only on group selection, neglecting simultaneous within-group variable selection.

Purpose of the Study:

  • To propose a novel adaptive group bridge method for simultaneous selection of groups and within-group variables in competing risks data.
  • To address the gap in methods that handle both between-group and within-group variable selection concurrently.

Main Methods:

  • Developed an adaptive group bridge method applicable to independent and clustered data.
  • The method allows the number of variables to increase with sample size.
  • Investigated asymptotic properties, including variable selection consistency at both group and within-group levels.

Main Results:

  • The proposed method demonstrates excellent asymptotic properties.
  • Achieved variable selection consistency at both group and within-group levels.
  • Showcased superior performance compared to group bridge, adaptive group lasso, and AIC/BIC-based methods in simulations and real data.

Conclusions:

  • The adaptive group bridge method offers a robust solution for simultaneous variable selection in competing risks data.
  • It provides enhanced accuracy and consistency over existing approaches.
  • The method is suitable for both independent and clustered data structures.