Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

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...

Assumptions of Survival Analysis

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.

Truncation in Survival Analysis

Truncation in Survival Analysis

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 observed.

Introduction To Survival Analysis

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 Groups

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...

Poisson Probability Distribution

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...

You might also read

Related Articles

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

Sort by

Same author

Web-based versus simulation-based refresher training in newborn life support: A randomized multicenter noninferiority controlled trial.

Resuscitation plus·2026

Same author

Evaluating machine- and deep learning approaches for artifact detection in infant EEG: classifier performance, certainty, and training size effects.

Biomedical physics & engineering express·2025

Same author

[Statement on the lower age limit for outpatient adenotomies and tonsillotomies].

Laryngo- rhino- otologie·2023

Same author

Structure and establishment of the German Cochlear Implant Registry (DCIR).

HNO·2023

Same author

Conception and implementation of a certification system for quality control of cochlear implant treatment in Germany.

HNO·2023

Same author

Treatment management and outcome of polytraumatized patients in a German certified trauma center - comparing standard versus maximum care.

Acta orthopaedica Belgica·2023

Search research articles

Related Experiment Video

Updated: Jun 19, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

A bivariate survival model with compound Poisson frailty.

A Wienke¹, S Ripatti, J Palmgren

¹Institute of Medical Epidemiology, Biostatistics and Informatics, University Halle-Wittenberg, Germany. andreas.wienke@medizin.uni-halle.de

Statistics in Medicine

|October 27, 2009

Summary

This study introduces a new correlated frailty model for analyzing time-to-event data, allowing for non-susceptible individuals. The model estimates that 15% of women are susceptible to breast cancer.

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Related Experiment Videos

Last Updated: Jun 19, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Biostatistics
Epidemiology
Survival Analysis

Background:

Traditional survival analysis often assumes universal susceptibility to an event.
Bivariate time-to-event data requires specialized modeling techniques.
Existing correlated frailty models may not account for non-susceptible populations.

Purpose of the Study:

To propose a novel correlated frailty model for bivariate time-to-event data.
To extend the compound Poisson frailty model to a bivariate setting.
To incorporate a non-susceptible fraction into the population.

Main Methods:

Development of a bivariate correlated frailty model.
Extension of the compound Poisson frailty model.
Maximum likelihood estimation for model parameters.
Simulation studies to assess parameter estimation properties.

Main Results:

The proposed model accommodates a non-susceptible fraction, addressing a limitation in standard survival analysis.
The model includes correlated gamma and inverse Gaussian frailty models as special cases.
Application to Swedish twin data estimated a 15% breast cancer susceptibility rate.

Conclusions:

The new correlated frailty model provides a flexible framework for bivariate survival data with non-susceptible individuals.
The model offers valuable insights into population susceptibility for diseases like breast cancer.
This approach enhances the analysis of time-to-event data in epidemiological studies.