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

500
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...
500
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

720
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...
720
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

493
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.
493
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

724
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...
724
Cancer Survival Analysis01:21

Cancer Survival Analysis

846
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...
846
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Randomized Trial of Hood With or Without Wing Attachment-Assisted Colonoscopy With Linked Color Imaging and Computer-Aided in Adenoma Detection.

Endoscopy international open·2026
Same author

Incidence of Clinically Documented Phantom Limb Pain During Hospitalization and Preoperative Risk Factors in Patients Who Underwent Nontraumatic Major Lower Limb Amputation: A Single-Center, Retrospective Study.

Medicina (Kaunas, Lithuania)·2026
Same author

Feasibility and treatment outcomes of simultaneous definitive chemoradiotherapy for elderly patients with synchronous hypopharyngeal and esophageal squamous cell carcinoma.

Journal of radiation research·2026
Same author

Extracorporeal Cardiopulmonary Resuscitation for Perioperative Cardiac Arrest in Noncardiac Surgery: A Nationwide Cohort Study in Japan.

Anesthesiology open·2026
Same author

Feasibility Study on Development of a Collaborative Monitoring System between a Hospital and Community Pharmacy Using an Electronic Patient-Reported Outcome Application for Breast Cancer Outpatients Treated with Abemaciclib.

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan·2026
Same author

Mediastinal lymph node dissection in older adults with radiologically pure-solid stage I lung cancer.

Surgical oncology·2026

Related Experiment Video

Updated: Apr 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

11.1K

Comparison of splitting methods on survival tree.

Asanao Shimokawa, Yohei Kawasaki, Etsuo Miyaoka

    The International Journal of Biostatistics
    |April 8, 2015
    PubMed
    Summary

    This study compares nine splitting criteria for survival trees, recommending specific methods based on hazard rate patterns (constant, decreasing, or increasing) for accurate survival time modeling using covariates.

    Area of Science:

    • Biostatistics
    • Machine Learning
    • Survival Analysis

    Background:

    • Survival trees are essential for modeling survival time based on covariates.
    • Various splitting criteria exist for Classification and Regression Trees (CART) in survival analysis.
    • Previous comparative studies were limited in scope.

    Purpose of the Study:

    • To compare nine splitting criteria for constructing survival trees.
    • To identify optimal criteria based on different hazard rate assumptions.
    • To demonstrate the utility of survival trees in medical research.

    Main Methods:

    • Simulation studies were conducted to compare nine splitting criteria.
    • Non-parametric survival models were assumed for terminal nodes.

    More Related Videos

    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
    07:13

    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

    Published on: April 18, 2025

    872
    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

    1.0K

    Related Experiment Videos

    Last Updated: Apr 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

    11.1K
    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
    07:13

    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

    Published on: April 18, 2025

    872
    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

    1.0K
  • Criteria evaluated included exponential log-likelihood loss, log-rank test, deviance residual, martingale residual, and impurity measures.

    • Main Results:

    • For constant hazard data, criteria based on exponential log-likelihood loss, log-rank test, deviance residual, or martingale residual are recommended.
    • For decreasing hazard data, criteria using two-sample test statistics or squared deviance residual are optimal.
    • For increasing hazard data, criteria using exponential log-likelihood loss or combined impurity measures are best.

    Conclusions:

    • The choice of splitting criterion significantly impacts survival tree performance.
    • Specific criteria are optimal depending on the underlying hazard rate of the data.
    • Survival trees offer a valuable tool for medical research and survival data analysis.