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

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

Parametric Survival Analysis: Weibull and Exponential Methods

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...
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.
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,...
Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
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

Machine Learning and Artificial Intelligence in Nutrition Research: Analytical Methods, Applications, and Key Considerations.

The Journal of nutrition·2026
Same author

Interpretability of an FDA-authorized AI/ML sepsis diagnostic tool improved by SHAP values.

JAMIA open·2026
Same author

Design characteristics of sequential multiple assignment randomised trials (SMARTs) for human health: a scoping review of studies between 2009 and 2024.

BMJ open·2025
Same author

Effects Among the Affected.

Statistics in medicine·2025
Same author

Using precision medicine methods to identify disease stages for chronic limb-threatening ischemia in participants of the BEST-CLI trial.

Journal of vascular surgery·2025
Same author

Determining optimal diet/exercise treatment assignment for patients with symptomatic knee osteoarthritis using baseline gait forces.

Osteoarthritis and cartilage open·2025
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
See all related articles

Related Experiment Video

Updated: May 17, 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

Recursively Imputed Survival Trees.

Ruoqing Zhu1, Michael R Kosorok

  • 1Ruoqing Zhu is a doctoral student, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 ( rzhu@live.unc.edu ). Michael R. Kosorok is Professor and Chair, Department of Biostatistics, and Professor, Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 ( kosorok@unc.edu ).

Journal of the American Statistical Association
|November 6, 2012
PubMed
Summary
This summary is machine-generated.

We introduce recursively imputed survival tree (RIST) regression, a new method for handling censored data. RIST improves model fit and reduces prediction error by effectively utilizing censored data through recursive imputation and randomized trees.

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

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 17, 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

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

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:

  • Statistics
  • Machine Learning
  • Survival Analysis

Background:

  • Survival data analysis often involves censored observations, posing challenges for traditional regression models.
  • Existing tree-based methods have limitations in effectively utilizing censored data.

Purpose of the Study:

  • To propose a novel nonparametric regression method, recursively imputed survival tree (RIST) regression, for analyzing right-censored data.
  • To enhance the utilization of censored data in survival analysis compared to existing tree-based approaches.

Main Methods:

  • Development of a novel recursive imputation technique integrated with extremely randomized trees.
  • Application of a Monte Carlo Expectation-Maximization (EM) algorithm framework for diverse tree-based fitting.
  • Validation through simulation studies and real-world data analyses.

Main Results:

  • The proposed RIST method demonstrates significantly improved utilization of censored data.
  • RIST yields enhanced model fit and reduced prediction error compared to previous tree-based methods.
  • Simulation studies and data analyses confirm the superior performance of RIST.

Conclusions:

  • RIST regression offers a powerful and effective new approach for survival data analysis with right-censored observations.
  • The method's ability to better leverage censored data leads to improved predictive accuracy and model performance.
  • RIST represents a significant advancement in nonparametric regression for survival analysis.