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

Convergent Evolution01:54

Convergent Evolution

33.0K
Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.
33.0K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

803
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...
803
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

607
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...
607
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

623
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...
623
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

432
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.
432
Cancer Survival Analysis01:21

Cancer Survival Analysis

769
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...
769

You might also read

Related Articles

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

Sort by
Same author

Multidimensional evaluation of large language models on the AAP in-service examination: Assessing accuracy, calibration, and citation reliability.

PLOS digital health·2026
Same author

Insulin Hypersecretion and Increased Ectopic Fat in South Asian American Adolescents and Young Adults Compared With White and African American Peers: The CHARISMA Study.

Diabetes care·2026
Same author

Clinical relevance of CompEx Asthma and impact on disease trajectory: benralizumab effect.

ERJ open research·2026
Same author

Brain atrophy staging in spinocerebellar ataxia type 3 for clinical prognosis and trial enrichment.

EBioMedicine·2025
Same author

An experimental medicine protocol for exploring the haemodynamic effects of dual agonism at the glucagon-like peptide-1 and glucagon receptor in healthy subjects.

British journal of clinical pharmacology·2025
Same author

Response to Biologics Along a Gradient of T2 Involvement in Patients With Severe Asthma: A Data-Driven Biomarker Clustering Approach.

The journal of allergy and clinical immunology. In practice·2025

Related Experiment Video

Updated: Feb 8, 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

Modelling converging hazards in survival analysis.

Peter Barker1, Robin Henderson

  • 1Department of Mathematics and Statistics, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK. peter.n.barker@astrazeneca.com

Lifetime Data Analysis
|October 1, 2004
PubMed
Summary
This summary is machine-generated.

This study introduces a novel mixed model for survival analysis, extending the Burr model to handle both proportional and converging hazards. This approach offers a flexible alternative to standard models for analyzing complex survival data, such as hospice patient survival times.

More Related Videos

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

6.9K
Blue-hazard-free Candlelight OLED
10:18

Blue-hazard-free Candlelight OLED

Published on: March 19, 2017

9.9K

Related Experiment Videos

Last Updated: Feb 8, 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
Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

6.9K
Blue-hazard-free Candlelight OLED
10:18

Blue-hazard-free Candlelight OLED

Published on: March 19, 2017

9.9K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • The Cox proportional hazards model is the standard for survival analysis but may not capture all hazard patterns.
  • Converging hazards are frequently observed in survival data and require specialized modeling techniques.
  • The Burr model, derived from gamma frailty, is a tool for modeling converging hazards.

Purpose of the Study:

  • To introduce a mixed model that extends the Burr model.
  • To accommodate both proportional and converging hazards within a single statistical framework.
  • To demonstrate the utility of this mixed model for survival data analysis.

Main Methods:

  • The study outlines the Burr model and its derivation from a gamma frailty model.
  • A novel mixed model is introduced, building upon the Burr model.
  • The mixed model is shown to be derivable via a gamma frailty interpretation, suggesting an expectation-maximization (E-M) fitting procedure.

Main Results:

  • The proposed mixed model effectively handles both proportional and converging hazards.
  • The gamma frailty interpretation provides a viable method for fitting the mixed model.
  • The modeling techniques are illustrated using real-world survival data from hospice patients.

Conclusions:

  • The developed mixed model offers a more flexible approach to survival analysis than traditional methods.
  • This model is particularly useful for datasets exhibiting both proportional and converging hazard patterns.
  • The application to hospice patient survival data demonstrates the model's practical relevance and effectiveness.