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...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
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...
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.
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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...

You might also read

Related Articles

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

Sort by
Same author

Pervasive mixed infections and recurrent begomovirus co-detection in symptomatic pumpkin plants from Guangxi, China.

Frontiers in microbiology·2026
Same author

Hybrid Supervised-Unsupervised Modeling for Post-Hurricane Private Well Contamination Risk Score Using Empirical Validation and Community-Informed Assessment.

GeoHealth·2026
Same author

Luteolin as a novel therapeutic for diabetic kidney disease: Targeting the ADAM10-TREM2 pathway.

Journal of advanced research·2026
Same author

Apparent impact sound insulation performance of raised discrete floating assemblies on mass timber floors.

Building acoustics (Brentwood, England)·2026
Same author

Structure-based design of an opioid receptor modulator for enhanced morphine analgesia.

Science advances·2026
Same author

miR-19b-3p regulates autophagy of BMSCs in hypoxia through PTEN/Akt/mTOR pathway to promote fracture healing.

Journal of molecular histology·2026
Same journal

A SEQUENTIAL SIGNIFICANCE TEST FOR TREATMENT BY COVARIATE INTERACTIONS.

Statistica Sinica·2026
Same journal

DEFINING AND ESTIMATING PRINCIPAL STRATUM SPECIFIC NATURAL MEDIATION EFFECTS WITH SEMI-COMPETING RISKS DATA.

Statistica Sinica·2026
Same journal

Longitudinal Modeling of Rank-based Global Outcome.

Statistica Sinica·2026
Same journal

INTEGRATING INCOMPLETE DATA FOR MEDIATION ANALYSIS.

Statistica Sinica·2026
Same journal

COMMUNITY EXTRACTION OF NETWORK DATA UNDER STOCHASTIC BLOCK MODELS.

Statistica Sinica·2026
Same journal

STATISTICAL INFERENCE FOR MEAN FUNCTIONS OF COMPLEX 3D OBJECTS.

Statistica Sinica·2025
See all related articles

Related Experiment Video

Updated: May 9, 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

VARIABLE SELECTION FOR CENSORED QUANTILE REGRESION.

Huixia Judy Wang1, Jianhui Zhou, Yi Li

  • 1Department of Statistics, North Carolina State University hwang3@ncsu.edu.

Statistica Sinica
|July 13, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel adaptive-lasso method for variable selection in survival analysis with censored data. The new procedure offers more accurate prognostic factor identification for patient survival times.

Keywords:
Conditional Kaplan-Meierdimension reductionkernelquantile regressionsurvival analysisvariable selection

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

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: May 9, 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

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Genomic Medicine

Background:

  • Quantile regression is valuable for linking patient survival times to demographic and genomic data.
  • Limited variable selection methods exist for quantile regression in survival analysis with censored outcomes.

Purpose of the Study:

  • To develop a new adaptive-lasso-based variable selection procedure for quantile regression with censored outcomes.
  • To improve the identification of prognostic factors influencing patient survival.

Main Methods:

  • Utilized adaptive-lasso for variable selection in quantile regression.
  • Incorporated redistribution-of-mass and effective dimension reduction to handle random censoring with multivariate covariates.
  • Demonstrated asymptotic model selection consistency.

Main Results:

  • The proposed procedure achieves model selection consistency.
  • Requires fewer assumptions than existing methods, leading to more accurate variable selection.
  • Successfully identified key factors associated with distinct patient survival sub-populations in a cancer trial.

Conclusions:

  • The novel adaptive-lasso procedure enhances variable selection accuracy in survival analysis for censored data.
  • This method aids oncologists in distinguishing prognostic factors for short and long survival patient groups.
  • Offers a more robust and less assumption-dependent approach compared to current techniques.