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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
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...
1.2K
Hazard Rate01:11

Hazard Rate

473
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
473
Survival Tree01:19

Survival Tree

463
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...
463
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

915
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...
915
Censoring Survival Data01:09

Censoring Survival Data

628
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...
628
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

691
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...
691

You might also read

Related Articles

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

Sort by
Same author

An Overview and Recent Developments in the Analysis of Multistate Processes.

Statistics in medicine·2026
Same author

Disease severity among hospitalized children during the COVID-19 pandemic in Israel.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology·2026
Same author

Assessing the Benefits and Burdens of Preventive Interventions.

Statistics in medicine·2026
Same author

Clinical Manifestations.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Hybrid and vaccination immunity against severe COVID-19 in the postpandemic era: author's response.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases·2025
Same author

Mastering rare event analysis: subsample-size determination in Cox and logistic regressions.

Biometrics·2025
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Mar 16, 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

11.0K

A quantile regression model for failure-time data with time-dependent covariates.

Malka Gorfine1, Yair Goldberg2, Ya'acov Ritov3

  • 1Department of Statistics and Operation Research, Tel Aviv University, Ramat Aviv, 6997801 Tel Aviv, Israel gorfinem@post.tau.ac.il.

Biostatistics (Oxford, England)
|August 4, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new quantile regression model for survival data with time-dependent covariates. The model uses instrumental variables and a doubly-robust estimator for accurate analysis of censored survival data.

Keywords:
Instrumental variablesQuantile regressionSurvival analysisTime-dependent covariates

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

2.7K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Related Experiment Videos

Last Updated: Mar 16, 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

11.0K
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

2.7K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Econometrics

Background:

  • Survival data analysis often involves covariates that change over time.
  • Time-dependent covariates require specialized statistical modeling techniques.
  • Quantile regression provides a flexible framework for analyzing survival data.

Purpose of the Study:

  • To develop a novel quantile regression model for survival data with time-dependent covariates.
  • To accommodate right-censored survival data within the quantile regression framework.
  • To provide robust estimation methods for analyzing such data.

Main Methods:

  • Development of a new quantile regression model for time-dependent covariates.
  • Utilizing instrumental variables for a simplified estimation technique.
  • Introduction of a doubly-robust estimator for enhanced data analysis.
  • Rigorous asymptotic and simulation studies to validate estimators.

Main Results:

  • The proposed model effectively handles time-dependent covariates in survival analysis.
  • The doubly-robust estimator demonstrates strong performance in simulations.
  • The methodology is validated using the Stanford heart transplant dataset.

Conclusions:

  • The novel quantile regression model offers a flexible and robust approach for analyzing survival data with time-dependent covariates.
  • The proposed estimation techniques, including the doubly-robust estimator, are suitable for right-censored data.
  • The methodology has practical utility in real-world applications, as shown by the heart transplant data analysis.