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

779
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...
779
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

232
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.
232
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

331
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,...
331
Survival Tree01:19

Survival Tree

208
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...
208
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

470
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...
470
Contingency Table01:29

Contingency Table

3.1K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
3.1K

You might also read

Related Articles

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

Sort by
Same author

Can the All of Us sample be reweighted to mirror a nationally representative sample? A comparison of mortality predictors.

Epidemiology (Cambridge, Mass.)·2026
Same author

Global Sensitivity Analysis for Studies Extending Inferences From a Randomized Trial to a Target Population.

Statistics in medicine·2026
Same author

Improvements in Cogstate Test Performance Depend on Number and Frequency of Prior Tests: Evidence from a Randomized Follow-Up Design.

Alzheimer disease and associated disorders·2026
Same author

Post-Discharge Anti-Seizure Medication Use Improves Post-Stroke Survival: <i>An Emulated Target Trial in Older Adults</i>.

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

Men's preconception diet quality patterns predict supportive food parenting practices: evidence from a longitudinal cohort study.

The international journal of behavioral nutrition and physical activity·2026
Same author

Clarifying the 'set to zero' approach for time-varying prenatal exposures.

International journal of epidemiology·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Nov 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

10.5K

Modeling semi-competing risks data as a longitudinal bivariate process.

Daniel Nevo1, Deborah Blacker2,3, Eric B Larson4

  • 1Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel.

Biometrics
|April 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical framework for analyzing Alzheimer's disease (AD) and death as semi-competing risks. The novel approach models dependence between AD onset and mortality, offering deeper clinical insights beyond traditional methods.

Keywords:
alzheimer's diseaseb-splinesdiscrete-time survivallongitudinal modelingpenalized maximum likelihood

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

2.3K
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

10.8K

Related Experiment Videos

Last Updated: Nov 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

10.5K
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

2.3K
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

10.8K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Neuroscience

Background:

  • Alzheimer's disease (AD) progression and mortality are often analyzed using competing risks models.
  • Standard competing risks methods treat the dependence between events as a nuisance, limiting clinical insights.
  • Semi-competing risks framework allows for the analysis of dependence between an event and its competing event.

Purpose of the Study:

  • To propose a novel regression-based framework for analyzing semi-competing risks in Alzheimer's disease research.
  • To represent and interpret the dependence structure between Alzheimer's disease onset and death.
  • To investigate the influence of gender and APOE ε4 allele on the joint risk of AD and death.

Main Methods:

  • Development of a novel regression framework based on a longitudinal bivariate process.
  • Representation of dependence in two forms: local and global dependence.
  • Estimation via penalized maximum likelihood, accommodating censoring, truncation, and time-varying covariates.
  • Sensitivity analyses to address potential ambiguities in likelihood contributions.

Main Results:

  • The proposed framework provides intuitive clinical interpretations for local and global dependence.
  • The framework was applied to the Adult Changes in Thought study data.
  • Analysis investigated the joint risk of AD and death concerning gender and APOE ε4 status.

Conclusions:

  • The novel statistical framework enhances the understanding of the relationship between Alzheimer's disease and mortality.
  • Modeling dependence in semi-competing risks offers valuable clinical insights.
  • The study highlights the importance of considering gender and APOE ε4 in the joint risk assessment of AD and death.