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

Survival Tree01:19

Survival Tree

119
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...
119
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

492
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...
492
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

160
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.
160
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

301
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...
301
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

228
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...
228
Cancer Survival Analysis01:21

Cancer Survival Analysis

402
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...
402

You might also read

Related Articles

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

Sort by
Same author

Association between CMR-derived pulmonary artery pulse wave velocity and pulmonary risk factors: the multi-ethnic study of atherosclerosis COPD study.

Magnetic resonance imaging·2026
Same author

Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.

Statistical methods in medical research·2026
Same author

Real-World Evaluation of Talquetamab for the Treatment of Relapsed/Refractory Multiple Myeloma (RRMM): An International Myeloma Working Group Immunotherapy Registry Real-World Analysis.

American journal of hematology·2026
Same author

Clonal Selection and Evolution after Treatment of Severe Aplastic Anemia.

NEJM evidence·2026
Same author

Erratum: Clinically relevant cut-points for changes in the Liver Frailty Index are associated with waitlist mortality in patients with cirrhosis.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society·2026
Same author

Erratum: The Liver Frailty Index enhances mortality risk prediction above and beyond MELD 3.0 alone.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society·2026

Related Experiment Video

Updated: Jul 27, 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

DYNAMIC RISK PREDICTION TRIGGERED BY INTERMEDIATE EVENTS USING SURVIVAL TREE ENSEMBLES.

Yifei Sun1, Sy Han Chiou2, Colin O Wu3

  • 1Department of Biostatistics, Columbia University.

The Annals of Applied Statistics
|June 7, 2023
PubMed
Summary

This study introduces a novel framework for dynamic risk prediction using survival tree ensembles. It enables personalized predictions that update with new patient data, improving accuracy for time-varying health information.

Keywords:
Dynamic predictionlandmark analysismulti-state modelsurvival treetime-dependent predictors

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

2.1K
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

307

Related Experiment Videos

Last Updated: Jul 27, 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
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

2.1K
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

307

Area of Science:

  • Biostatistics
  • Health Informatics
  • Clinical Epidemiology

Background:

  • Electronic health records and registry databases provide vast patient data.
  • Improving risk prediction with time-varying patient information is a key research area.
  • Conventional landmark prediction uses fixed times, limiting adaptability.

Purpose of the Study:

  • To develop a unified framework for landmark prediction using survival tree ensembles.
  • To enable updated risk predictions as new patient information becomes available.
  • To allow subject-specific, event-triggered landmark times, overcoming fixed-time limitations.

Main Methods:

  • A nonparametric, risk-set-based ensemble procedure is proposed.
  • Martingale estimating equations from individual trees are averaged.
  • The framework handles both longitudinal predictors and right-censored event time outcomes.

Main Results:

  • Extensive simulation studies demonstrate the method's performance.
  • The approach is applied to Cystic Fibrosis Foundation Patient Registry (CFFPR) data.
  • Dynamic prediction of lung disease and identification of prognosis factors in cystic fibrosis patients are achieved.

Conclusions:

  • The developed framework offers a flexible and powerful tool for dynamic risk prediction.
  • It effectively incorporates time-varying data and subject-specific events.
  • The methods advance the prediction of clinical outcomes in complex datasets.