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...
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...
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.
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...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

You might also read

Related Articles

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

Sort by
Same author

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.

Journal of machine learning research : JMLR·2026
Same author

Medicare Insurance Type and Broad Genomic Profiling in Metastatic Cancer.

JAMA network open·2026
Same author

Doubly Robust Estimators of the Restricted Mean Time in Favor Estimands in Individual- and Cluster-Randomized Trials.

Statistics in medicine·2026
Same author

JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.

The annals of applied statistics·2026
Same author

Subgroup Analysis of Differential Networks with Latent Variables.

Statistics and computing·2026
Same author

Robust Heterogeneity Adjustment for Gaussian Graphical Model With Latent Variables.

Statistics in medicine·2026

Related Experiment Video

Updated: May 8, 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 IN PARTLY LINEAR REGRESSION MODEL WITH DIVERGING DIMENSIONS FOR RIGHT CENSORED DATA.

Shuangge Ma1, Pang Du

  • 1School Public Health, Yale University, New Haven, CT 06520, U.S.A.

Statistica Sinica
|August 20, 2013
PubMed
Summary

This study introduces a novel semiparametric regression model for prognosis studies, effectively analyzing both low-dimensional clinical and high-dimensional gene expression data for improved risk factor assessment.

Keywords:
Semiparametric regressioniterated Lassoright censored datavariable 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 8, 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
  • Genomics
  • Medical Prognosis

Background:

  • Biomedical research frequently involves analyzing diverse risk factor datasets, including clinical, environmental, and gene expression data.
  • Prognosis studies often contend with right-censored response variables, necessitating specialized statistical methodologies.

Purpose of the Study:

  • To propose a flexible semiparametric regression model capable of integrating low-dimensional and high-dimensional covariates for prognosis.
  • To develop a penalized variable selection approach for identifying significant risk factors in complex biomedical datasets.

Main Methods:

  • A semiparametric regression model with distinct nonparametric and parametric components for low- and high-dimensional covariates, respectively.
  • An iterated Lasso approach for penalized variable selection in the parametric component, with proven selection consistency.
  • A sieve approach for estimating the nonparametric component and an empirical model selection tool based on Kullback-Leibler geometry.

Main Results:

  • The proposed semiparametric model effectively handles both low-dimensional clinical/environmental and high-dimensional gene expression data.
  • The iterated Lasso approach demonstrated selection consistency for parametric covariate effects.
  • Numerical studies indicated satisfactory performance of the developed methodology.

Conclusions:

  • The proposed semiparametric regression model offers a robust framework for prognosis studies with mixed-dimensional risk factors.
  • The method provides a powerful tool for variable selection and risk factor identification in complex biomedical data.
  • The approach was successfully illustrated through an application to a lymphoma study.