Jove
Visualize
Contact Us

Related Concept Videos

Survival Tree01:19

Survival Tree

439
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...
439
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

621
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...
621
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

438
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.
438
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

855
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...
855
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

643
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...
643
Life Histories01:29

Life Histories

23.0K
Overview
23.0K

You might also read

Related Articles

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

Sort by
Same author

Calcium/calmodulin-dependent protein kinase II links ER stress with Fas and mitochondrial apoptosis pathways.

The Journal of clinical investigation·2009
Same author

Cripto-1 overexpression is involved in the tumorigenesis of nasopharyngeal carcinoma.

BMC cancer·2009
Same author

Range of motion and orientation of the lumbar facet joints in vivo.

Spine·2009
Same author

[Silencing of COX-2 in nasopharyngeal carcinoma cells with a shRNAmir lentivirus vector].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2009
Same author

The risk of melamine-induced nephrolithiasis in young children starts at a lower intake level than recommended by the WHO.

Pediatric nephrology (Berlin, Germany)·2009
Same author

Adult scoliosis in patients over sixty-five years of age: outcomes of operative versus nonoperative treatment at a minimum two-year follow-up.

Spine·2009
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 Experiment Video

Updated: Feb 16, 2026

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

937

Survival Forests with R-Squared Splitting Rules.

Hong Wang1, Xiaolin Chen2, Gang Li3

  • 11 School of Mathematics and Statistics, Central South University , Changsha, China .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|December 22, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new survival forest model using pseudo R-squared splitting rules. The novel approach demonstrates superior performance over existing survival models for censored data analysis.

Keywords:
R-squaredcensored datarandom survival forestsplitting functiontime-to-event data

More Related Videos

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

10.9K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Related Experiment Videos

Last Updated: Feb 16, 2026

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

937
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

10.9K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Area of Science:

  • Biostatistics
  • Machine Learning
  • Data Science

Background:

  • Survival forest models offer a flexible nonparametric alternative for analyzing censored data, especially when parametric assumptions are violated.
  • Traditional survival models may struggle with misspecified function forms or violated underlying assumptions.
  • Censored data analysis is crucial in fields like medicine, engineering, and finance.

Purpose of the Study:

  • To propose a novel survival forest approach for modeling censored data.
  • To introduce and evaluate a new pseudo R-squared splitting rule for constructing survival trees.
  • To compare the performance of the proposed model against established survival models.

Main Methods:

  • Development of a survival forest algorithm incorporating a novel pseudo R-squared splitting criterion.
  • Implementation and testing of the proposed model on well-known benchmark datasets.
  • Comparative analysis using the C-index metric against Random Survival Forest, Cox proportional hazard model, and Generalized Boosted Model.

Main Results:

  • The proposed survival forest model with pseudo R-squared splitting rules generally outperformed existing popular survival models.
  • The C-index metric indicated superior predictive accuracy for the novel approach on benchmark datasets.
  • The model's effectiveness was demonstrated in handling censored data scenarios.

Conclusions:

  • The novel survival forest approach with pseudo R-squared splitting rules is a competitive and effective method for censored data analysis.
  • This method offers an improved alternative to traditional parametric, semiparametric, and existing nonparametric survival models.
  • The findings suggest potential for wider adoption in biostatistics and other fields dealing with censored data.