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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

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 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.
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...
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...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

You might also read

Related Articles

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

Sort by
Same author

Maternal paracetamol use during pregnancy and child IQ at age 7: A prospective cohort study.

Early human development·2026
Same author

Real-world heterogeneity in the prognostic value of pre-transplant flow cytometry measurable residual disease in acute myeloid leukemia in first complete remission: CIBMTR analysis.

Haematologica·2026
Same author

A case-control study of the association between per- and polyfluoroalkyl substances (PFAS) and risk of postmenopausal breast cancer in the Danish diet cancer and health cohort.

Environmental research·2026
Same author

Clinical Factors and Biomarkers During Pregnancy and Risk of Cardiovascular Disease.

JAMA cardiology·2026
Same author

Prenatal paracetamol modulates sexually dimorphic behaviour and steroidogenesis in adult male and female mice.

Reproduction (Cambridge, England)·2026
Same author

Outcomes of myeloablative allogeneic hematopoietic cell transplantation with omidubicel vs alternative donor sources.

Blood neoplasia·2026
Same journal

Individualized dynamic latent factor model for multi-resolutional data with application to mobile health.

Biometrika·2026
Same journal

Functional principal component analysis forsparse censored data.

Biometrika·2026
Same journal

Finding distributions that differ, with false discovery rate control.

Biometrika·2026
Same journal

Sequential Gibbs posteriors with applications to principal component analysis.

Biometrika·2026
Same journal

Comparing causal parameters with many treatments and positivity violations.

Biometrika·2026
Same journal

Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials.

Biometrika·2026
See all related articles

Related Experiment Video

Updated: May 12, 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

A semiparametric random effects model for multivariate competing risks data.

Thomas H Scheike1, Yanqing Sun, Mei-Jie Zhang

  • 1Department of Biostatistics , University of Copenhagen , Øster Farimagsgade 5, Copenhagen DK-1014 , Denmark ts@biostat.ku.dk.

Biometrika
|April 25, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing multiple causes of failure, enhancing understanding of competing risks. The model reveals associations between different failure types, particularly in twin studies of menopause onset.

Keywords:
Binomial modellingCopula functionCross-odds ratioCumulative incidence functionDanish twin dataEstimating equationInverse-censoring probability weightingTwo-stage estimation

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

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

Related Experiment Videos

Last Updated: May 12, 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

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

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

Area of Science:

  • Biostatistics and Epidemiology
  • Statistical Modeling

Background:

  • Analyzing multivariate competing risks data is crucial for understanding complex health outcomes.
  • Existing models may not fully capture the nuances of cause-specific failure associations, especially in clustered data.

Purpose of the Study:

  • To propose a novel semiparametric random effects model for multivariate competing risks.
  • To investigate associations between cause-specific failure times using a new cross-odds ratio measure.
  • To apply the model to Danish twin data for analyzing natural menopause onset.

Main Methods:

  • Developed a semiparametric random effects model where marginal cumulative incidence functions follow a generalized additive model.
  • Utilized copula functions with cluster-level covariates to model dependence between cause-specific failure times.
  • Employed a two-stage estimation procedure for marginal models and dependence parameters, with derived large sample properties.

Main Results:

  • The proposed model effectively estimates marginal cumulative incidence functions and dependence parameters.
  • A novel cross-odds ratio measure quantifies associations between cause-specific failure times.
  • Application to Danish twin data successfully modeled cumulative incidence of natural menopause and its intra-pair associations.

Conclusions:

  • The semiparametric random effects model provides a flexible framework for multivariate competing risks analysis.
  • The cross-odds ratio offers a valuable tool for interpreting cause-specific failure time associations.
  • The methodology is effective for analyzing clustered data, as demonstrated in the twin study of menopause.