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

Censoring Survival Data01:09

Censoring Survival Data

625
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
625
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

687
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...
687
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

683
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,...
683
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

1.2K
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
1.2K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

472
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.
472
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

316
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
316

You might also read

Related Articles

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

Sort by
Same author

Inflammatory Biomarkers and Their Associations with Arrhythmic Burden Following SGLT2-I Treatment in Chronic Heart Failure-A Subanalysis of the ERASe Trial.

Journal of clinical medicine·2026
Same author

A feature selection-based oblique hyperplane for oblique random survival forests.

BMC medical research methodology·2026
Same author

Increased urinary IgM excretion in patients with obstructive sleep apnea.

Respiratory medicine·2026
Same author

Impact of Ertugliflozin on Cardiac Structure and Function in Patients with ICDs/CRT-Ds Assessed by Echocardiography: A Post Hoc Sub-Analysis of the ERASe Trial.

Journal of clinical medicine·2025
Same author

Comparison of oblique random survival forest, random survival forest, and statistical models for time-to-event data using simulation study.

Scientific reports·2025
Same author

Mitigating Night Biomass Loss in Outdoor Pilot-Scale Mixotrophic Algal Cultivation of Monoraphidium minutum Using Flue Gas Condensate and Cheese Whey.

Biotechnology and bioengineering·2025

Related Experiment Video

Updated: Mar 12, 2026

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

2.7K

Diagnostic checks in mixture cure models with interval-censoring.

Sylvie Scolas1, Catherine Legrand1, Abderrahim Oulhaj2

  • 11 Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Université Catholique de Louvain, Louvain-la-Neuve, Belgium.

Statistical Methods in Medical Research
|November 6, 2016
PubMed
Summary

This study introduces diagnostic methods for mixture cure models with interval-censored survival data. These techniques assess model assumptions, crucial for analyzing cure fractions in diseases like Alzheimer's.

Keywords:
Diagnosticscureintervalparametricresiduals

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K
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

11.2K

Related Experiment Videos

Last Updated: Mar 12, 2026

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

2.7K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K
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

11.2K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Mixture cure models are used for interval-censored survival data with a cured fraction.
  • Assessing the goodness-of-fit for these models is challenging.
  • Validating mixture cure models requires checking assumptions for both latency and incidence parts.

Purpose of the Study:

  • To adapt and evaluate Cox-Snell and deviance residuals for diagnosing mixture cure models.
  • To assess the performance of these residuals in detecting model assumption departures.
  • To apply these diagnostic techniques to real-world Alzheimer's disease data.

Main Methods:

  • Adaptation of Cox-Snell and deviance residuals for interval-censored data with unknown cure status.
  • Extensive simulation studies to evaluate residual performance.
  • Application of developed methods to Alzheimer's disease patient data.

Main Results:

  • Cox-Snell and deviance residuals can be effectively adapted for mixture cure model diagnostics.
  • Simulations demonstrate the ability of these residuals to detect misspecification.
  • The techniques provide valuable insights when applied to Alzheimer's data.

Conclusions:

  • Adapted residuals offer a robust approach for validating mixture cure models with interval-censored data.
  • These methods are essential for reliable analysis of cure fractions in survival data.
  • The study contributes to improved statistical modeling in chronic disease research.