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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

225
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...
225
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

157
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.
157
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

182
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,...
182
Causality in Epidemiology01:21

Causality in Epidemiology

482
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
482
Cancer Survival Analysis01:21

Cancer Survival Analysis

386
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
386
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

2.1K
A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
2.1K

You might also read

Related Articles

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

Sort by
Same author

Amazon infrastructure poses biosecurity risks.

Science (New York, N.Y.)·2026
Same author

Modified Skew Discrete Laplace Regression Models for Integer-Valued Data With Applications to Paired Samples.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

A shared frailty regression model for clustered survival data.

Statistical methods in medical research·2025
Same author

The Shared Weighted Lindley Frailty Model for Clustered Failure Time Data.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

High transmission of SARS-CoV-2 in Amazonia, Brazil: an epidemiological strategy to contain severe cases of COVID-19.

Journal of public health policy·2024
Same author

A New Mixture Model With Cure Rate Applied to Breast Cancer Data.

Biometrical journal. Biometrische Zeitschrift·2024
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
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
See all related articles

Related Experiment Video

Updated: Jul 22, 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

10.2K

Cure rate models for heterogeneous competing causes.

Márcia Brandão1, Jeremias Leão1, Diego Ignacio Gallardo2

  • 1Departamento de Estatística, Universidade Federal do Amazonas, Manaus, Brazil.

Statistical Methods in Medical Research
|July 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel cure rate model using finite mixture distributions for competing causes, improving analysis of time-to-event data. The proposed method enhances cure rate modeling by accounting for individual variations in competing causes.

Keywords:
Concurrent causesexpectation–maximization algorithmmelanoma data setmixturespower series distribution

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

2.1K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

305

Related Experiment Videos

Last Updated: Jul 22, 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

10.2K
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

2.1K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

305

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Cure rate models analyze time-to-event data with a cured patient fraction.
  • Traditional models assume a random variable for concurrent causes, which may not reflect individual differences.
  • Individual variations in competing causes distribution are common in practice.

Purpose of the Study:

  • To propose a novel cure rate model using finite mixture distributions for competing causes.
  • To model the number of malignant cells using a mixture of two power series distributions.
  • To allow the proportion of cured competing causes to depend on covariates for direct cure rate modeling.

Main Methods:

  • Utilized a finite mixture of competing causes distributions.
  • Assumed a Weibull distribution for time-to-event data.
  • Employed an expectation-maximization algorithm for parameter estimation.
  • Conducted Monte Carlo simulations to evaluate the estimation method's performance.

Main Results:

  • The proposed model encompasses several existing models and introduces new ones.
  • The expectation-maximization algorithm effectively estimates model parameters.
  • Monte Carlo simulations demonstrated the method's viability.
  • Application to cutaneous melanoma data showed superior model fitting compared to traditional methods.

Conclusions:

  • The proposed finite mixture cure rate model offers a flexible and powerful approach for analyzing time-to-event data.
  • It accurately models individual variations in competing causes.
  • The model shows practical utility and improved performance in real-world epidemiological studies.