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

Censoring Survival Data01:09

Censoring Survival Data

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 reasons...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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

Comparing the Survival Analysis of Two or More Groups

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 Cox...

You might also read

Related Articles

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

Sort by
Same author

Variable selection in mixture cure models using elastic net penalty: application to COVID-19 data.

PloS one·2025
Same author

Differential methylation region detection via an array-adaptive normalized kernel-weighted model.

PloS one·2024
Same author

Analysis of IBNR Liabilities with Interevent Times Depending on Claim Counts.

Methodology and computing in applied probability·2022
Same author

Diet alters age-related remodeling of aortic collagen in mice susceptible to atherosclerosis.

American journal of physiology. Heart and circulatory physiology·2020
Same author

Transforming Growth Factor Beta3 is Required for Cardiovascular Development.

Journal of cardiovascular development and disease·2020
Same author

Both diet and Helicobacter pylori infection contribute to atherosclerosis in pre- and postmenopausal cynomolgus monkeys.

PloS one·2019

Related Experiment Video

Updated: May 17, 2026

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

Semiparametric Estimation with Recurrent Event Data under Informative Monitoring.

Akim Adekpedjou1, Edsel A Peña

  • 1akima@mst.edu . Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, MO 65409.

Journal of Nonparametric Statistics
|October 18, 2012
PubMed
Summary

This study introduces new statistical methods for analyzing recurrent event data under a generalized Koziol-Green structure. The research provides efficient estimators for event distributions and parameters, with performance evaluated via simulations.

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

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: May 17, 2026

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

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

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Statistics
  • Survival Analysis
  • Reliability Engineering

Background:

  • Recurrent event data analysis is crucial in fields like medicine and engineering.
  • Existing methods often assume specific distributional forms or lack efficiency.
  • The generalized Koziol-Green (GKG) structure offers a flexible modeling framework.

Purpose of the Study:

  • To develop and evaluate statistical estimators for recurrent event data under the GKG structure.
  • To estimate the underlying event distribution function (F) and a key parameter (β).
  • To compare the efficiency of proposed estimators against existing nonparametric methods.

Main Methods:

  • Utilized Nelson-Aalen and product-limit type estimators for survival functions.
  • Derived estimators for the parameter β within the GKG model.
  • Employed asymptotic and small-sample analyses, including simulation studies.

Main Results:

  • Obtained consistent and efficient estimators for the event distribution (F) and parameter β.
  • Demonstrated the performance of estimators under the GKG assumption.
  • Evaluated estimator behavior when the GKG structure is violated.

Conclusions:

  • The proposed estimators are effective for recurrent event data analysis under the GKG model.
  • The study provides valuable insights into the efficiency and robustness of these statistical methods.
  • Further research can explore extensions to more complex event data scenarios.