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

363
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...
363
Hazard Rate01:11

Hazard Rate

266
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...
266
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

253
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.
253
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

516
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...
516
Hazard Ratio01:12

Hazard Ratio

378
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
378
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

405
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...
405

You might also read

Related Articles

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

Sort by
Same author

Continuous electricity from charged total dissolved solids in wastewater using a wood-based ion-selective power generator.

Nature communications·2026
Same author

Reprogramming Acetaminophen Metabolism via Amide-to-Thioamide Modification to Prevent Drug-Induced Liver Injury.

Journal of medicinal chemistry·2026
Same author

The effectiveness of a plant-based milk with fermented brown rice on constipation symptoms via gut microbiota modulation: a double-blind randomized controlled trial.

European journal of nutrition·2026
Same author

Covalent Interaction Between High-Amylose Corn Starch and Ferulic Acid: Reshaping of the Structure.

Foods (Basel, Switzerland)·2026
Same author

Subgroup Analysis of Interval-censored Failure Time Data With Application to Alzheimer's Disease.

Statistics in medicine·2026
Same author

Clitocine suppresses TNBC progression by boosting CCRL2 to block survival signals and neutrophil-driven inflammation.

Journal of biological engineering·2026

Related Experiment Video

Updated: Nov 20, 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

10.6K

An additive hazards cure model with informative interval censoring.

Shuying Wang1, Chunjie Wang2, Jianguo Sun3

  • 1School of Mathematics and Statistics, Changchun University of Technology, Changchun, 130012, China.

Lifetime Data Analysis
|January 22, 2021
PubMed
Summary

This study introduces a new method for analyzing survival data with interval censoring and informative censoring, addressing a gap in existing statistical procedures. The proposed approach effectively estimates cure rates and failure times in complex survival scenarios.

Keywords:
Cure modelEM algorithmInformative interval censoringSieve estimation

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

2.4K
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

398

Related Experiment Videos

Last Updated: Nov 20, 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

10.6K
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

2.4K
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

398

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Cured subgroups are common in survival studies, but analysis is challenging with interval-censored data.
  • Informative censoring complicates survival data analysis, lacking established estimation procedures.
  • Existing methods often fail to adequately address both interval censoring and informative censoring simultaneously.

Purpose of the Study:

  • To develop a robust statistical method for survival data with interval censoring and informative censoring.
  • To provide an estimation procedure for situations where a subgroup of subjects may be cured.
  • To analyze complex survival data encountered in medical research, such as the cardiac allograft vasculopathy study.

Main Methods:

  • A three-component model combining logistic regression for cure rate, additive hazards for failure time, and nonhomogeneous Poisson for observation.
  • A sieve maximum likelihood estimation (SMLE) procedure for parameter estimation.
  • An expectation-maximization (EM) algorithm for implementing the proposed estimation approach.

Main Results:

  • The proposed sieve maximum likelihood estimation procedure provides consistent estimators.
  • Asymptotic properties of the estimators derived from the SMLE procedure are established.
  • Simulation studies demonstrate the effectiveness of the proposed method in practical settings.

Conclusions:

  • The developed statistical framework offers a viable solution for survival data with interval censoring and informative censoring.
  • The EM algorithm facilitates the practical application of the proposed estimation method.
  • The approach is validated through application to a real-world medical study, showing its utility.