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

451
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...
451
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

287
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.
287
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

568
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...
568
Censoring Survival Data01:09

Censoring Survival Data

416
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...
416
Survival Tree01:19

Survival Tree

294
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...
294
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

29.9K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
29.9K

You might also read

Related Articles

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

Sort by
Same author

Enhancing Outcome Measurement in Oncology Clinical Trials Through Artificial Intelligence: A Scoping Review.

JCO clinical cancer informatics·2026
Same author

Baseline Patient Characteristics and Transplant-Related Factors Predicting Post-Transplant Spirometry Decline and Recovery in Allogeneic Hematopoietic Stem Cell Transplant Recipients.

Transplantation and cellular therapy·2026
Same author

Adverse event profile following maintenance olaparib in patients with BRCA-mutated platinum-sensitive relapsed serous ovarian cancer in the phase III SOLO2 trial.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society·2026
Same author

Time to achieve response and depth of maximal tumor response as potential surrogates for overall survival in advanced non-small cell lung cancer.

Future oncology (London, England)·2026
Same author

Polyfunctional T peripheral helper cells are associated with the magnitude and durability of antibody responses after COVID-19.

Clinical & translational immunology·2025
Same author

Aspiration Pneumonia Related Surgical Deaths-A Review of a 10 Year Australian State-Based Mortality Audit.

ANZ journal of surgery·2025

Related Experiment Video

Updated: Dec 7, 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

10.6K

Recurrent time-to-event models with ordinal outcomes.

Val Gebski1, Karen Byth1, Rebecca Asher1

  • 1NHMRC Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia.

Pharmaceutical Statistics
|October 2, 2020
PubMed
Summary

This study extends ordinal time-to-event models to analyze recurrent events, offering richer insights into disease progression and treatment effects beyond single events. The new models provide probabilities for event severity across multiple recurrences over time.

Keywords:
conditional modelcontinuation ratio modelgap modelmarginal modelmultiple eventsordinal outcometime-to-event data

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

2.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K

Related Experiment Videos

Last Updated: Dec 7, 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

10.6K
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

2.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Clinical Research Methodology

Background:

  • Traditional time-to-event models often simplify outcomes to single events, limiting their utility in complex disease trajectories.
  • Recurrent events and event severity provide more comprehensive clinical information, particularly in chronic diseases and oncology.
  • Existing ordinal models have not fully addressed the complexities of recurrent events.

Purpose of the Study:

  • To extend ordinal time-to-event models to accommodate multiple and recurrent events.
  • To develop statistical frameworks for both marginal and conditional analyses of recurrent ordinal outcomes.
  • To provide methods for estimating and interpreting probabilities of event severity over time for recurrent events.

Main Methods:

  • Proposed an extension of the ordinal time-to-event framework for recurrent events.
  • Developed marginal models where all subjects remain at risk for subsequent events.
  • Developed conditional models where risk is dependent on prior events.
  • Utilized instantaneous baseline hazard estimates for both marginal and conditional approaches.
  • Outlined methods for constructing confidence intervals for event probabilities.

Main Results:

  • The extended models provide estimates for the probabilities of an event of each severity for each recurrence over time.
  • The approach allows for both marginal and conditional analyses, catering to different study designs.
  • Methods for fitting these models and interpreting results are demonstrated.

Conclusions:

  • The proposed extension enhances the analysis of time-to-event data with recurrent and ordinal outcomes.
  • These models offer valuable tools for understanding disease patterns and treatment efficacy in clinical studies.
  • The methods provide a more nuanced understanding of event risk and severity across multiple occurrences.