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

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
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
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
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

589
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...
589
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
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

You might also read

Related Articles

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

Sort by
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
Same author

Transfer learning estimation of the accelerated failure time model based on high-dimensional data.

Biometrics·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

Nonparametric tests for stratified additive hazards model based on current status data.

Xiaodong Fan1,2, Shi-Shun Zhao1, Qingchun Zhang1,2

  • 1Center for Applied Statistical Research and College of Mathematics, Jilin University, Changchun, People's Republic of China.

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

This study introduces a new statistical test for analyzing current status data in stratified regression models, specifically addressing the additive hazards model. The method effectively detects stratum effects in medical research, proving useful in real-world applications.

Keywords:
Additive hazards modelcurrent status datainformative censoring

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
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

2.2K

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
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
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

2.2K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Stratified regression models are common in medical research with data from diverse sources like multiple centers.
  • Testing for stratum effects is crucial but lacks established procedures for current status data under additive hazards models.

Purpose of the Study:

  • To develop and validate a novel statistical test for detecting stratum effects in additive hazards models with current status data.
  • To provide a much-needed method for analyzing data where subjects' event status is only known at a single time point.

Main Methods:

  • The study proposes a new test procedure for the additive hazards model.
  • Asymptotic distributions for the test statistic are derived and provided.
  • A simulation study was conducted to assess the method's performance.

Main Results:

  • The proposed test procedure is shown to be effective in detecting stratum effects.
  • Simulation results indicate good performance across various practical scenarios.
  • The method was successfully applied to real current status data from a tumorigenicity study.

Conclusions:

  • A new, validated statistical test is now available for analyzing stratum effects in additive hazards models with current status data.
  • This research fills a gap in existing statistical methodologies for survival data analysis.
  • The findings have direct implications for analyzing complex medical and biological datasets.