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

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 Cox...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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 until a...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Cancer Survival Analysis01:21

Cancer Survival Analysis

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...
Hazard Rate01:11

Hazard Rate

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...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

You might also read

Related Articles

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

Sort by
Same author

Problem-solving therapy versus supportive psychotherapy for Veterans with moderate suicide risk and chronic pain: A pilot randomized clinical trial.

Behaviour research and therapy·2026
Same author

Per- and Polyfluoroalkyl Substances (PFAS) Exposure Profiles and Their Predictors in a Study of US Volunteer Firefighters.

American journal of industrial medicine·2026
Same author

A cross-sectional analysis of low-cost non-cancer registry recruitment sources for a hybrid type 2 trial evaluating a digital intervention for melanoma survivors.

mHealth·2026
Same author

Actigraphy-based differences in sleep in those receiving opioid versus non-opioid therapy postsurgical removal of impacted third molars: a pilot study from the opioid analgesic reduction study (OARS).

Sleep medicine·2026
Same author

Illness perceptions and behavioural responses as mechanisms of change in problem-solving treatment for Veterans with Gulf War Illness.

British journal of health psychology·2026
Same author

Analgesic Differences in Males and Females After Third Molar Surgery: A Subgroup Analysis of the OARS Randomized Clinical Trial.

JAMA network open·2025

Related Experiment Video

Updated: Jul 18, 2026

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

Case-cohort designs and analysis for clustered failure time data.

Shou-En Lu1, Joanna H Shih

  • 1Department of Biostatistics, University of Medicine and Dentistry of New Jersey, New Brunswick, New Jersey 08903, USA. lus2@umdnj.edu

Biometrics
|December 13, 2006
PubMed
Summary

This study introduces novel case-cohort designs for analyzing multiple health outcomes simultaneously. These methods efficiently assess risk factors for rare diseases in large populations, improving statistical power and reducing costs.

Related Experiment Videos

Last Updated: Jul 18, 2026

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

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • The case-cohort design is efficient for studying rare diseases in large cohorts.
  • Existing case-cohort methods are limited to univariate failure time data.
  • There is a need for efficient designs for multivariate failure time data.

Purpose of the Study:

  • To propose novel case-cohort designs adapted for multivariate failure time data.
  • To develop an estimation procedure for regression parameters in marginal proportional hazards models.
  • To evaluate the statistical properties and performance of the proposed estimators.

Main Methods:

  • Adaptation of the case-cohort design for multivariate failure time data.
  • Utilizing an independence working model for estimation.
  • Estimation of regression parameters in marginal proportional hazards models with unspecified correlation structure.
  • Development of statistical properties for proposed estimators.

Main Results:

  • The proposed case-cohort designs are suitable for multivariate failure time data.
  • The independence working model approach provides valid estimation.
  • Simulation studies demonstrate the performance and efficiency of the proposed estimators.
  • The methodology is illustrated using a real-world dataset.

Conclusions:

  • The proposed case-cohort designs offer an efficient approach for analyzing multivariate failure time data.
  • This methodology enhances the study of risk factors for infrequent diseases with multiple outcomes.
  • The approach provides a flexible framework for survival data analysis in complex studies.