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

Hazard Rate01:11

Hazard Rate

159
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
159
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

525
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...
525
Random Variables01:09

Random Variables

13.2K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
13.2K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

213
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,...
213
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

173
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.
173
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

772
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
772

You might also read

Related Articles

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

Sort by
Same author

A meta-analysis of the long-term effects of antihypertensive therapy on the risk of major cardiovascular disease across 51 randomized trials.

Nature medicine·2026
Same author

Vaccination-Related Applications and Health Care Professionals' Observed Changes in Human Papillomavirus Vaccine Hesitancy: Cross-Sectional Survey.

JMIR mHealth and uHealth·2026
Same author

Smart Pediatric Oncology Tracker of Symptoms (SPOTS), a Web-Based Interface for the Pediatric PRO-CTCAE: Development and Usability Study.

JMIR formative research·2026
Same author

Untangling Impacts of Socioeconomic Position, Chronic Disease, and Low-Level PM2.5 Exposure on Mortality Among Native American Medicare Beneficiaries.

International journal of environmental research and public health·2026
Same author

Pharmacological blood-pressure lowering for the prevention of cardiovascular disease and death across the full spectrum of chronic kidney disease severity: an individual-participant data meta-analysis.

Lancet (London, England)·2026
Same author

Estimating the Effect of Pravastatin versus Usual Care Under Full Adherence in the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial-Lipid Lowering Trial (ALLHAT-LLT).

American journal of epidemiology·2026
Same journal

Correction to: Home dampness and molds and occurrence of respiratory tract infections in the first 27 years of life: the Espoo Cohort Study.

American journal of epidemiology·2026
Same journal

A SIMPLE AND POWERFUL TEST OF VACCINE WANING.

American journal of epidemiology·2026
Same journal

Association Between maternal body mass index, offspring growth and pubertal timing: results from a longitudinal birth cohort study.

American journal of epidemiology·2026
Same journal

Correction to: Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study.

American journal of epidemiology·2026
Same journal

RE: advancing observational research on arts and health: theory-informed approaches using the RADIANCE framework.

American journal of epidemiology·2026
Same journal

Maternal Cesarean Section and Offspring ASD or ADHD Risk: A Nurses' Health Study II Analysis.

American journal of epidemiology·2026
See all related articles

Related Experiment Video

Updated: Aug 16, 2025

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

2.2K

A Dynamic Risk Model for Multitype Recurrent Events.

Alokananda Ghosh, Wenyaw Chan, Naji Younes

    American Journal of Epidemiology
    |December 22, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new statistical model for analyzing multiple types of recurrent events, even when a final event stops further occurrences. The model helps predict event-free survival by considering prior events and subject characteristics.

    Keywords:
    absolute riskmultitype recurrent eventsrisk modelsrobust standard errorssurvival analysisterminating events

    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

    10.3K
    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

    10.7K

    Related Experiment Videos

    Last Updated: Aug 16, 2025

    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

    2.2K
    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

    10.3K
    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

    10.7K

    Area of Science:

    • Biostatistics
    • Longitudinal Data Analysis
    • Survival Analysis

    Background:

    • Multitype recurrent events are common in longitudinal studies.
    • Prior events, including terminating ones like death or cure, often influence the risk of subsequent events.
    • Existing models may not fully capture the complex interplay of multiple event types and their impact on risk.

    Purpose of the Study:

    • To propose a flexible joint model for multitype recurrent events.
    • To estimate the change in risk associated with prior events and subject characteristics.
    • To predict event-free survival probabilities over time.

    Main Methods:

    • Development of a fully parametric joint multitype recurrent-events model.
    • Explicit estimation of risk changes due to event history and covariates.
    • Construction of standard likelihood functions and robust standard errors.
    • Application and illustration using data from the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial.

    Main Results:

    • The proposed model provides estimates for risk changes associated with prior events (number and type).
    • It allows for the estimation of absolute risk for each event type (terminating and nonterminating).
    • The model successfully predicts event-free survival probabilities.
    • Demonstrated utility in a real-world clinical trial setting.

    Conclusions:

    • The flexible joint multitype recurrent-events model offers a comprehensive approach to analyzing complex event data.
    • It enhances understanding of risk dynamics influenced by event history and individual characteristics.
    • The model is valuable for predicting future event occurrences and survival probabilities in longitudinal studies.