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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
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...
1.2K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

473
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.
473
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

688
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...
688
Survival Curves01:18

Survival Curves

828
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
828
Finding Critical Values for Chi-Square01:18

Finding Critical Values for Chi-Square

4.6K
Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
4.6K
Quartile01:15

Quartile

10.0K
Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
10.0K

You might also read

Related Articles

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

Sort by
Same author

Toolkit for multistate disease progression simulation and treatment decision-making aid.

BMC medical research methodology·2026
Same author

Competing-triggering effect models for multitype recurrent event data.

Biometrics·2026
Same author

Systemic Lipid Peroxidation and Colorectal Cancer Risk: A Time-Varying Relationship.

International journal of cancer·2026
Same author

Improving Overall Risk Ranking via Subgroup-Level Information Borrowing in Survival Risk Stratification.

Statistics and its interface·2026
Same author

Making LLM Predictions Interpretable: Fine-Tuning GPT-4o for Early Discontinuation of Cancer Medication.

Studies in health technology and informatics·2026
Same author

Ampere-Level Syngas Synthesis by Controllable Active Hydrogen Supply to Regulate CO<sub>2</sub> Reduction Depth on High-Entropy (CuZnAlZrCe)O<sub>2</sub> Oxide Nanosheets.

Angewandte Chemie (International ed. in English)·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Mar 15, 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

11.0K

Quantile association for bivariate survival data.

Ruosha Li1, Yu Cheng2, Qingxia Chen3

  • 1Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, Texas, U.S.A.

Biometrics
|September 10, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new quantile association measure for bivariate survival data, revealing dynamic relationships between event times. The methods accurately assess disease onset associations, like myocardial infarction and stroke risk.

Keywords:
AssociationBivariate survival dataCopulaOdds ratioQuantiles

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

2.7K
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

748

Related Experiment Videos

Last Updated: Mar 15, 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

11.0K
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
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

748

Area of Science:

  • Biostatistics and Survival Analysis
  • Epidemiology and Public Health
  • Cardiovascular Research

Background:

  • Bivariate survival data are common in chronic disease studies and clinical trials with multiple endpoints.
  • Understanding the association between two event times is crucial for scientific insight and patient care.
  • Existing methods may not fully capture the dynamic nature of associations across different event time quantiles.

Purpose of the Study:

  • To introduce a novel quantile association measure for bivariate survival data.
  • To develop and evaluate nonparametric and semiparametric estimators for this measure.
  • To investigate the dynamic association patterns between different event times, such as myocardial infarction and stroke.

Main Methods:

  • Development of a distribution-free quantile association measure.
  • Proposal of a nonparametric estimator for the quantile association measure.
  • Introduction of a semiparametric estimator offering improved smoothness and efficiency.
  • Assessment of asymptotic properties including uniform consistency and root-n convergence.

Main Results:

  • The proposed quantile association measure effectively describes dynamic associations as a function of quantile levels.
  • Both nonparametric and semiparametric estimators demonstrate satisfactory numerical performance across various dependence structures.
  • The methods exhibit desirable asymptotic properties, ensuring statistical reliability.
  • Application to an atherosclerosis study revealed significant association patterns between time to myocardial infarction and time to stroke.

Conclusions:

  • The novel quantile association measure provides a flexible and powerful tool for analyzing bivariate survival data.
  • The proposed estimators are statistically sound and perform well in practice.
  • This approach offers valuable insights into the complex relationships between chronic disease events.