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...
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...
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.
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...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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,...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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 observed.

You might also read

Related Articles

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

Sort by
Same author

Equivalence of in vitro blood clot mechanical properties using different anticoagulants.

Journal of the mechanical behavior of biomedical materials·2026
Same author

Cancer risk with methotrexate-TNF antagonist combination therapy compared to TNF-antagonist monotherapy in IBD.

Inflammatory bowel diseases·2026
Same author

Toward Artificial Intelligence-driven Clinical Decision Support Tools in Rheumatology.

Rheumatic diseases clinics of North America·2026
Same author

Fine particulate matter exposure and long-term lung-function trajectory in adults with cystic fibrosis.

Annals of the American Thoracic Society·2026
Same author

Geographical and Language-Based Factors Associated with Neurodevelopmental Follow-Up in Children with Congenital Heart Disease.

Pediatric cardiology·2026
Same author

Statistics and AI - A Fireside Conversation.

Harvard data science review·2026

Related Experiment Video

Updated: May 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

Evaluating incremental values from new predictors with net reclassification improvement in survival analysis.

Yingye Zheng1, Layla Parast, Tianxi Cai

  • 1Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA. yzheng@fhcrc.org

Lifetime Data Analysis
|December 21, 2012
PubMed
Summary

This study introduces new statistical methods to evaluate how well new disease prediction markers improve risk assessments over existing ones. These robust procedures enhance the clinical usefulness of predictive models for better patient outcomes.

More Related Videos

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

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

Related Experiment Videos

Last Updated: May 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

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

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

Area of Science:

  • Biostatistics
  • Medical Informatics
  • Epidemiology

Background:

  • Individualized disease risk prediction is crucial in modern medicine.
  • Assessing the clinical utility of novel biomarkers against existing variables is essential.
  • Net Reclassification Improvement (NRI) is a key metric for comparing risk models.

Purpose of the Study:

  • To propose novel nonparametric and semiparametric methods for calculating NRI.
  • To extend NRI calculations for censored failure time outcomes and covariate-dependent censoring.
  • To provide more robust and efficient procedures for risk model evaluation.

Main Methods:

  • Development of nonparametric and semiparametric procedures for NRI calculation.
  • Incorporation of censored failure time outcomes.
  • Accommodation of covariate-dependent censoring.

Main Results:

  • Proposed methods demonstrated good performance in finite sample simulations.
  • The new procedures offer more robust and potentially more efficient estimations compared to existing methods.
  • Illustrative application to a cardiovascular disease onset prediction model.

Conclusions:

  • The developed statistical procedures effectively evaluate improvements in risk prediction models.
  • These methods enhance the assessment of clinical usefulness for new biomarkers.
  • The approach is applicable to predicting various diseases, including cardiovascular disease.