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

221
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...
221
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

194
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.
194
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

277
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...
277
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

384
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...
384
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

254
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,...
254
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

534
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...
534

You might also read

Related Articles

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

Sort by
Same author

In silico characterization of acetyl-CoA carboxylase from Staphylococcus aureus and Escherichia coli: A comparative analysis.

Computational biology and chemistry·2026
Same author

Association of parental death and separation with child mortality in India.

Scientific reports·2026
Same author

Substrate and target selectivity of 4'-fluoroadenosine against viral and host polymerases.

bioRxiv : the preprint server for biology·2026
Same author

The Q226H Mutation in Avian H5N1 Hemagglutinin Mediates a Path towards Structural Adaptation in Humans.

bioRxiv : the preprint server for biology·2026
Same author

Severity and determinants of depression among parents following child loss in Bankura District West Bengal.

Discover mental health·2026
Same author

Higher racial diversity in US business and law schools is linked to higher graduate salaries.

Nature·2026
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: Sep 8, 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

Analysis of interval-censored competing risks data under missing causes.

Debanjan Mitra1, Ujjwal Das1, Kalyan Das2

  • 1Operations Management, Quantitative Methods and Information Systems Area, Indian Institute of Management Udaipur, Udaipur, India.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a vertical modeling approach for analyzing interval-censored competing risks data with missing failure causes. The method effectively extracts maximum information, outperforming complete case analysis, especially for smaller sample sizes.

Keywords:
Gompertz modelInterval censoringcompeting risksconfidence intervalscumulative incidence functionmaximum likelihood estimates

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
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

372

Related Experiment Videos

Last Updated: Sep 8, 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
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

372

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Competing risks data present challenges in survival analysis, particularly when failure causes are missing.
  • Interval censoring further complicates analysis, requiring specialized statistical methods.

Purpose of the Study:

  • To propose and evaluate a novel vertical modeling approach for interval-censored competing risks data with missing failure causes.
  • To assess the performance of the proposed method compared to traditional complete case analysis.

Main Methods:

  • Development of a vertical modeling framework to maximize data utilization.
  • Estimation of model parameters using maximum likelihood.
  • Construction of asymptotic confidence intervals via observed Fisher information and parametric bootstrap.
  • Simulation studies to evaluate estimator performance and assess model misspecification effects.

Main Results:

  • The proposed vertical modeling approach demonstrates superior performance over complete case analysis.
  • The method is particularly beneficial for smaller sample sizes where complete case analysis can lead to significant inferential errors.
  • Monte Carlo simulations confirmed the robustness of the approach regarding cumulative incidence function estimation.

Conclusions:

  • The vertical modeling technique is a valuable tool for analyzing interval-censored competing risks data with missing failure causes.
  • This methodology enhances analytical accuracy and reliability, especially in data-scarce scenarios.