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

600
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...
600
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

196
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.
196
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

280
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...
280
Relative Risk01:12

Relative Risk

340
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
340
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

252
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,...
252
Survival Curves01:18

Survival Curves

308
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
308

You might also read

Related Articles

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

Sort by
Same author

Dynamics of infection, vaccination and excess mortality during the COVID-19 pandemic among older individuals-a nationwide analysis.

European journal of epidemiology·2026
Same author

An extraction pipeline for analysis of hematopoietic stem cell transplantation data.

Bone marrow transplantation·2026
Same author

The Impact of Two Data-Generating Processes for Competing Risk Data on the Discrimination and Calibration of Two Types of Competing Risk Regression Models.

Statistics in medicine·2026
Same author

Estimating conditional survival benefit for the allocation of scarce resources.

Statistical methods in medical research·2026
Same author

Discrimination performance in illness-death models with interval-censored disease data.

Statistical methods in medical research·2026
Same author

Variable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data.

Biometrical journal. Biometrische Zeitschrift·2025
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

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

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
Same journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
Same journal

Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Sep 9, 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

10.3K

Competing risks models with two time scales.

Angela Carollo1,2, Hein Putter2, Paul Hc Eilers3

  • 1Laboratory of Fertility and Well-Being, Max Planck Institute for Demographic Research, Germany.

Statistical Methods in Medical Research
|September 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new competing risks model using two time scales, like age and time since diagnosis, to better understand cancer mortality. The model effectively analyzes complex survival data, improving accuracy in risk prediction.

Keywords:
Cause-specific hazardsP-splinescancer mortalitypenalised composite link modeltwo-dimensional smoothing

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

2.2K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K

Related Experiment Videos

Last Updated: Sep 9, 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

10.3K
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
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Competing risks models often use a single time scale, limiting their application in complex scenarios like cancer mortality.
  • Jointly considering multiple time scales (e.g., age and time since diagnosis) is crucial for accurately assessing cause-specific hazards.
  • Existing methods for multiple time scales in competing risks are limited, necessitating novel approaches.

Purpose of the Study:

  • To propose and implement a flexible statistical model for competing risks analysis incorporating two time scales.
  • To estimate cause-specific hazards that vary smoothly over two dimensions using penalized splines.
  • To address challenges with coarsely grouped data in real-world datasets like the SEER program.

Main Methods:

  • Developed a novel competing risks model utilizing two-dimensional P-splines for hazard smoothing.
  • Leveraged the equivalence between hazard smoothing and Poisson regression for estimation.
  • Employed generalized linear array models for computational efficiency and a penalized composite link model for data ungrouping.
  • Implemented the model in the R-package TwoTimeScales.

Main Results:

  • The proposed model effectively estimates cause-specific hazards across two time scales.
  • The method successfully handles coarsely grouped data, demonstrated using SEER breast cancer mortality data.
  • The R-package TwoTimeScales provides a practical tool for applying this advanced statistical methodology.

Conclusions:

  • The novel two-time scale competing risks model offers a significant advancement for analyzing complex survival data.
  • This approach enhances the understanding of mortality patterns in diseases like breast cancer by considering age and time since diagnosis.
  • The developed methodology and software facilitate more accurate risk assessment and epidemiological research.