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

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.

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...

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...

Multicompartment Models: Overview

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,...

Mechanistic Models: Overview of Compartment Models

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...

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...

You might also read

Related Articles

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

Sort by

Same author

An Overview and Recent Developments in the Analysis of Multistate Processes.

Statistics in medicine·2026

Same author

Censoring and Competing Risks: Avoidable and Non-Avoidable Events. Comment to the Article "Hazards constitute key quantities for analysing, interpreting and understanding time-to-event data" by Beyersmann, Schmoor, and Schumacher.

Biometrical journal. Biometrische Zeitschrift·2025

Same author

CEUS compared with CECT, MRI, and FDG-PET/CT for diagnosing CRC liver metastases: a diagnostic test accuracy systematic review and meta-analysis.

Expert review of gastroenterology & hepatology·2024

Same author

Cardiovascular events according to blood pressure thresholds recommended by ACC/AHA.

Blood pressure·2024

Same author

Simulation-based sample size calculations of marginal proportional means models for recurrent events with competing risks.

Pharmaceutical statistics·2024

Same author

Oxidative Stress-Induced Damage to RNA and DNA and Mortality in Individuals with Psychiatric Illness.

JAMA psychiatry·2024

Search research articles

Related Experiment Video

Updated: May 27, 2026

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

Interpretability and importance of functionals in competing risks and multistate models.

Per Kragh Andersen¹, Niels Keiding

¹Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark. p.k.andersen@biostat.ku.dk

Statistics in Medicine

|November 15, 2011

Summary

This study introduces three principles to assess the practical interpretability of model parameters in survival analysis and multistate models. These criteria help determine if derived parameters, like those in competing risks or illness-death models, are meaningful for real-world applications.

More Related Videos

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

Related Experiment Videos

Last Updated: May 27, 2026

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

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

Area of Science:

Biostatistics
Survival Analysis
Multistate Models

Background:

Transition hazards are fundamental parameters in survival analysis and multistate models, including competing risks and illness-death models.
Analysis often requires supplementary parameters derived from transition hazards (functionals).
Not all mathematically defined functionals are practically interpretable.

Purpose of the Study:

To propose criteria for evaluating the practical interpretability of functionals derived from transition hazards.
To ensure that supplementary model parameters are meaningful in real-world contexts.

Main Methods:

Development of three simple principles to serve as criteria for practical interpretability.
Application of these principles to assess functionals of transition hazards in survival and multistate models.

Main Results:

Identification of three key principles that can be used to check the practical interpretability of model parameters.
These principles offer a systematic way to evaluate the meaningfulness of derived parameters beyond their mathematical definition.

Conclusions:

The proposed principles provide a valuable tool for researchers using survival and multistate models.
Adherence to these criteria enhances the practical relevance and interpretability of complex statistical models in various scientific fields.