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

Hazard Ratio01:12

Hazard Ratio

686
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...
686
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

692
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...
692
Hazard Rate01:11

Hazard Rate

474
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...
474
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
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

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

Assumptions of Survival Analysis

475
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.
475

You might also read

Related Articles

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

Sort by
Same author

Negative prompt-guided optimization: Enhancing soft prompt generalization in vision-language models.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

E2T: EEG-to-Trajectory Transformer for Motor Imagery-Based Fully-DoF Motion Prediction.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Leveraging contextual confidence for smarter retrieval in large language models.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

SSF-SET: A Discrete EEG Token-based Framework for Sleep Stage Forecasting.

IEEE journal of biomedical and health informatics·2026
Same author

EEG-based Cross-subject Prediction for Consciousness State Transitions under Sedation using a Deep Learning Framework.

IEEE journal of biomedical and health informatics·2025
Same author

EEG-Translator: A Cross-Modality Framework for Subject-Specific EEG and Voice Reconstruction from Imagined Speech.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Related Experiment Video

Updated: Mar 19, 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

Bayesian test for hazard ratio in survival analysis.

Gwangsu Kim1, Seong-Whan Lee2

  • 1Department of Statistics, Seoul National University, 1 Gwanak-ro, Seoul, 151-742 Korea.

Springerplus
|June 23, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible Bayesian test for hazard function equivalence in survival analysis. The proposed method is more powerful and robust than existing tests, especially when proportional hazards are violated or functions cross.

Keywords:
B-splineBayesian testCrossing hazard functionsLog rank testPartial likelihoodProportional hazards modelTime-varying survival analysis

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
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.9K

Related Experiment Videos

Last Updated: Mar 19, 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
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.9K

Area of Science:

  • Statistics
  • Survival Analysis

Background:

  • Testing for equivalence of hazard functions is crucial in survival analysis.
  • Existing methods often struggle with violated proportional hazards assumptions or crossing hazard functions.

Purpose of the Study:

  • To propose a novel Bayesian test for hazard function equivalence.
  • To develop a flexible method that does not require weight determination when proportional hazards are violated.

Main Methods:

  • A Bayesian hypothesis testing framework was developed.
  • The proposed test was evaluated against existing methods using simulations and real-world data.

Main Results:

  • The proposed Bayesian test demonstrated greater power and robustness in detecting differences in hazard functions.
  • The test performed well even with crossing hazard functions and violated proportional hazards assumptions.
  • Numerical properties were found to be desirable.

Conclusions:

  • The proposed Bayesian test offers a powerful and robust alternative for assessing hazard function equivalence.
  • Its methodological flexibility makes it suitable for complex survival data scenarios.