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

468
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...
468
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
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

486
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.
486
Actuarial Approach01:20

Actuarial Approach

359
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
359
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

715
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,...
715
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

949
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...
949

You might also read

Related Articles

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

Sort by
Same author

Possible involvement of genome-wide near-haploidy and <i>MEN1</i> mutation in the molecular pathogenesis of pituitary carcinoma: Analysis by whole exome sequencing.

Neuro-oncology advances·2026
Same author

A multicenter randomized phase II/III trial of salvage treatment for refractory primary central nervous system lymphoma using tirabrutinib: JCOG2314 (ReSTART).

Japanese journal of clinical oncology·2026
Same author

Prediction of Overall Survival in Glioblastoma Using Early Postoperative Reduction in FLAIR Lesion Volume After Gross Total Resection.

Cancers·2026
Same author

Nationwide genomic data analysis of central nervous system tumors in Japan based on C-CAT database.

International journal of clinical oncology·2026
Same author

Comparing artificial intelligence and physician performance in predicting IDH mutation status in glioma.

NPJ digital medicine·2026
Same author

Significance of shunt placement for secondary communicating hydrocephalus in patients with high-grade glioma.

Journal of neuro-oncology·2026

Related Experiment Video

Updated: Mar 25, 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

Tree Based Method for Aggregate Survival Data Modeling.

Asanao Shimokawa, Yoshitaka Narita, Soichiro Shibui

    The International Journal of Biostatistics
    |February 17, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel survival tree model for aggregate data, allowing data points to belong to multiple nodes. This flexible approach enhances data interpretation for aggregate survival 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

    7.0K
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    3.1K

    Related Experiment Videos

    Last Updated: Mar 25, 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
    Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
    07:02

    Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

    Published on: January 19, 2019

    7.0K
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    3.1K

    Area of Science:

    • Biostatistics
    • Medical Informatics
    • Data Science

    Background:

    • Traditional medical research often treats individual patients as statistical units.
    • However, analyzing aggregate survival data requires treating data sets as statistical units, posing unique modeling challenges.

    Purpose of the Study:

    • To propose a new tree-structured model for aggregate survival data.
    • To develop a flexible hierarchical model that allows data points to be included in multiple terminal nodes.
    • To enhance the interpretability of aggregate survival data analysis.

    Main Methods:

    • Developed a novel tree-structured modeling approach for aggregate survival data.
    • Implemented a modified Kaplan-Meier method using expectation values for partially included concepts.
    • Applied the model to primary brain tumor patient data.

    Main Results:

    • The proposed model successfully constructed a tree structure from aggregate survival data.
    • Partial inclusion of data points in multiple nodes yielded a more flexible model.
    • The application to primary brain tumor data provided new interpretations compared to classical methods.

    Conclusions:

    • The novel survival tree model offers a flexible and interpretable approach for analyzing aggregate survival data.
    • This method enhances understanding of complex datasets where individual data points contribute to multiple concepts.
    • The approach provides valuable insights beyond traditional survival tree modeling.