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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

131
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.
131
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

239
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...
239
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

195
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...
195
Survival Tree01:19

Survival Tree

86
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...
86
Cancer Survival Analysis01:21

Cancer Survival Analysis

353
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...
353
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

209
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
209

You might also read

Related Articles

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

Sort by
Same author

Low-dose Empagliflozin Does Not Modify Myocardial Energetics and Function in a Large-animal Model of Hibernating Myocardium.

Journal of cardiovascular translational research·2026
Same author

Surgical Versus Transcatheter Aortic Valve Replacement in Bicuspid Aortic Stenosis: 1-Year Clinical Outcomes in Patients Aged 65 Years and Older.

Journal of the American Heart Association·2026
Same author

A high-fat diet nutritional intervention reprograms cardiac metabolism and improves systolic function in a pig model of heart failure with reduced ejection fraction.

Basic research in cardiology·2026
Same author

External Validation of Two Different Cardiac Damage Staging Systems for Aortic Stenosis in Patients Treated with Surgical Aortic Valve Replacement.

Journal of clinical medicine·2026
Same author

Aortic Stenosis-Associated Cardiac Damage: A Comparison Between Patients Treated with Surgery and Transcatheter Aortic Valve Replacement.

Journal of clinical medicine·2026
Same author

Surgical reconstruction of coexisting left ventricular aneurysm and pseudoaneurysm in a patient with ventricular tachycardia: a case report.

European heart journal. Case reports·2026

Related Experiment Video

Updated: Jul 5, 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

10.2K

Advanced considerations in survival analysis.

Manuel Carnero-Alcázar1, Lourdes Montero-Cruces1, Javier Cobiella-Carnicer1

  • 1Department of Cardiac Surgery, Hospital Clínico San Carlos, CardioRed1, Madrid, Spain.

European Journal of Cardio-Thoracic Surgery : Official Journal of the European Association for Cardio-Thoracic Surgery
|January 20, 2024
PubMed
Summary

This primer explains survival analysis in cardiovascular research, covering censoring and complex event analyses beyond standard Kaplan-Meier and Cox models. It details competing risks and alternatives for unmet proportional hazards assumptions.

Keywords:
competing risks analysisproportional hazards assumptionrestricted mean survival timesurvival analysis

More Related Videos

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

6.5K
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

290

Related Experiment Videos

Last Updated: Jul 5, 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

10.2K
Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

6.5K
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

290

Area of Science:

  • Cardiovascular research
  • Biostatistics
  • Survival analysis

Background:

  • Survival investigation is crucial in cardiovascular research, involving complexities like censoring and extended follow-up periods.
  • Standard methods like Kaplan-Meier and Cox models may be insufficient for complex survival data, especially with multiple event types.
  • Accurate interpretation of survival data requires understanding these intrinsic issues.

Purpose of the Study:

  • To provide a detailed guide on interpreting common survival analyses.
  • To introduce methods for analyzing competing risks in survival data.
  • To present alternatives to conventional survival methods when the proportional hazards assumption is violated.

Main Methods:

  • Review and explanation of standard survival analysis techniques (Kaplan-Meier, Cox models).
  • Detailed discussion on handling competing risks in survival data.
  • Exploration of alternative statistical models for situations where proportional hazards assumption is not met.

Main Results:

  • The primer offers clear interpretations of common survival analyses.
  • It presents practical approaches for analyzing competing risks, enhancing analytical depth.
  • Alternative methods are provided for scenarios where proportional hazards assumption fails.

Conclusions:

  • This primer enhances the understanding and application of survival analysis in cardiovascular research.
  • It equips researchers with tools to handle complex survival data, including competing risks and violated assumptions.
  • The content aims to improve the rigor and accuracy of survival outcome interpretation in clinical studies.