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

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

115
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...
115
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

55
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,...
55
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

57
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.
57
Longitudinal Studies01:26

Longitudinal Studies

81
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
81
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

237
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:
237
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

61
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,...
61

You might also read

Related Articles

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

Sort by
Same author

Real-time prediction of rapid weight change in children with cystic fibrosis who have initiated modulator therapy.

Clinical nutrition ESPEN·2026
Same author

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same author

Characterizing Documented Psychosocial Stressors in Pediatric Psychiatric Emergencies with an Open-Weight Large Language Model.

medRxiv : the preprint server for health sciences·2026
Same author

Evaluating Linkage Approaches for Address-Level Socioenvironmental Exposure Assessment.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Quantitative MRI Markers Detect Postpancreatitis Changes and Diabetes: A Prospective Cohort Study.

Clinical and translational gastroenterology·2026
Same author

Climate Change and Pediatric Population Health Management.

Pediatric clinics of North America·2026

Related Experiment Video

Updated: May 10, 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

10.0K

A Joint Model for (Un)Bounded Longitudinal Markers, Competing Risks, and Recurrent Events Using Patient Registry

Pedro Miranda Afonso1,2, Dimitris Rizopoulos1,2, Anushka K Palipana3,4

  • 1Department of Biostatistics, Erasmus University Medical Center, Rotterdam, the Netherlands.

Statistics in Medicine
|April 25, 2025
PubMed
Summary

This study introduces a new Bayesian joint model to analyze complex survival data, including recurrent and competing events, and bounded biomarkers. The model provides more precise insights into disease progression and biomarker associations.

Keywords:
bounded outcomescompeting riskscystic fibrosisjoint modelmultivariate longitudinal datarecurrent events

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

1.9K
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

10.6K

Related Experiment Videos

Last Updated: May 10, 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

10.0K
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

1.9K
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

10.6K

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Survival Analysis

Background:

  • Joint models for longitudinal and survival data are popular but struggle with complex data structures like recurrent and competing events.
  • Existing models often assume Gaussian distributions for biomarkers, which is unsuitable for bounded markers, leading to biased results.
  • Handling multiple bounded longitudinal markers alongside recurrent and competing events in a single model is challenging.

Purpose of the Study:

  • To propose a novel Bayesian shared-parameter joint model.
  • To accommodate multiple (possibly bounded) longitudinal markers, recurrent events, and competing risks simultaneously.
  • To improve the analysis of complex survival data and biomarker associations.

Main Methods:

  • Developed a Bayesian shared-parameter joint model.
  • Utilized the beta distribution for bounded longitudinal markers.
  • Incorporated recurrent event processes and competing risks.
  • Modeled various association forms, discontinuous risk intervals, and gap/calendar timescales.

Main Results:

  • A simulation study demonstrated superior performance compared to simpler joint models.
  • The model was applied to the US Cystic Fibrosis Foundation Patient Registry.
  • Quantified associations between lung function, BMI, and pulmonary exacerbations, accounting for competing risks of death and transplantation.

Conclusions:

  • The proposed model effectively handles complex survival data with multiple bounded markers and competing risks.
  • It offers more precise insights into disease progression, as shown in the Cystic Fibrosis Foundation Patient Registry analysis.
  • The efficient implementation in the R package JMbayes2 facilitates complex analyses.