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

Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
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...
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...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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

Introduction To Survival Analysis

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 until a...

You might also read

Related Articles

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

Sort by
Same author

Siderophore-producing bacteria reduce soil cadmium bioavailability and alleviate cadmium stress in alfalfa.

Ecotoxicology and environmental safety·2026
Same author

Dose-dependent association between opioid administration and ventilator-associated pneumonia in sepsis patients receiving mechanical ventilation.

Respiratory medicine·2026
Same author

Risk factors of bleeding in patients with atrial fibrillation undergoing percutaneous coronary intervention: an analysis from the MANJUSRI study.

Frontiers in cardiovascular medicine·2026
Same author

Proportional Hazards Regression for Interval-Censored Outcomes With an Interval-Censored Covariate.

Statistics in medicine·2026
Same author

Caution is warranted when interpreting the association of perioperative fluid balance and acute kidney injury in patients undergoing elective colorectal surgery.

European journal of anaesthesiology·2026
Same author

Expression of a recombinant lactoferrin N-terminal functional fragment in three expression systems and its efficacy against enterotoxigenic Escherichia coli K88 infection.

BMC veterinary research·2026

Related Experiment Video

Updated: Jul 6, 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

Boosting method for nonlinear transformation models with censored survival data.

Wenbin Lu1, Lexin Li

  • 1Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. lu@stat.ncsu.edu

Biostatistics (Oxford, England)
|March 18, 2008
PubMed
Summary

We introduce a new nonlinear transformation model for censored survival data analysis. This boosting algorithm effectively estimates covariate effects, even with high-dimensional data and fewer samples than 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

Related Experiment Videos

Last Updated: Jul 6, 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

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:

  • Biostatistics
  • Survival Analysis
  • Machine Learning

Background:

  • Censored survival data analysis is crucial in many scientific fields.
  • Existing models like proportional hazards and proportional odds have limitations.
  • High-dimensional covariates pose challenges for traditional survival models.

Purpose of the Study:

  • To propose a general class of nonlinear transformation models for censored survival data.
  • To develop a robust algorithm for nonparametric estimation of covariate effects.
  • To address challenges posed by high-dimensional covariates and small sample sizes.

Main Methods:

  • A cubic smoothing spline-based component-wise boosting algorithm is derived.
  • Nonparametric estimation of covariate effects using the gradient of the marginal likelihood.
  • Importance sampling is employed for efficient computation of the marginal likelihood.

Main Results:

  • The proposed method effectively handles censored survival data.
  • It demonstrates strong performance in estimating covariate effects, even in high-dimensional settings.
  • The algorithm is suitable for scenarios where the sample size is smaller than the number of predictors.

Conclusions:

  • The general nonlinear transformation model provides a flexible framework for survival data analysis.
  • The boosting algorithm offers a powerful tool for nonparametric covariate effect estimation.
  • The method shows promise for analyzing complex biological data, such as microarray survival data.