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
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

473
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.
473
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

692
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,...
692
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.7K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.7K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

680
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...
680

You might also read

Related Articles

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

Sort by
Same author

The Metabolic and Genetic Bases of High Carotenoid Deposition in Orange-Fleshed Potato Tuber.

Journal of agricultural and food chemistry·2026
Same author

Hspa1b attenuates hypoxia/reoxygenation-induced cardiomyocyte injury through dual suppression of P53-driven apoptotic and ferroptotic pathways.

Cell stress & chaperones·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

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

Respiratory medicine·2026
Same author

Multiscale Prediction of Tumor Micronecrosis and Progression in Clear Cell Renal Cell Carcinoma Based on Artificial Intelligence: A Multicenter Cohort Study.

Academic radiology·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

Related Experiment Video

Updated: Mar 16, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

LOCAL BUCKLEY-JAMES ESTIMATION FOR HETEROSCEDASTIC ACCELERATED FAILURE TIME MODEL.

Lei Pang1, Wenbin Lu1, Huixia Judy Wang1

  • 1Merck & Co., Inc, North Wales, PA 19454, U.S.A.; Department of Statistics, George Washington University, Washington, D.C. 20052, U.S.A.

Statistica Sinica
|August 23, 2016
PubMed
Summary

This study introduces a new statistical method for analyzing survival data, offering an interpretable alternative to traditional models. The local Buckley-James estimator effectively handles complex error patterns, improving accuracy in survival time predictions.

Keywords:
Accelerated failure time modelBuckley-James estimationHeteroscedasticityKernel estimationLocal Kaplan-MeierSurvival analysis

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

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

Related Experiment Videos

Last Updated: Mar 16, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
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

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • The accelerated failure time (AFT) model offers interpretable alternatives to the Cox proportional hazards model in survival analysis.
  • Existing AFT estimation methods often assume independent and identically distributed errors, which is frequently violated in real-world data.
  • Heteroscedasticity, where error variance depends on covariates, is a common issue in log survival times.

Purpose of the Study:

  • To develop a robust estimation method for the accelerated failure time model that accounts for heteroscedastic errors.
  • To establish theoretical properties, including consistency and asymptotic normality, of the proposed estimator.
  • To provide a practical inference approach using resampling techniques.

Main Methods:

  • Development of a local Buckley-James estimator tailored for AFT models with heteroscedastic errors.
  • Theoretical analysis to prove the consistency and asymptotic normality of the new estimator.
  • Implementation of a resampling strategy to facilitate statistical inference.

Main Results:

  • The proposed local Buckley-James estimator demonstrates consistency and asymptotic normality.
  • Simulation studies confirm the estimator's flexibility and improved efficiency in the presence of heteroscedasticity.
  • The method's utility is validated through application to a real-world breast cancer dataset.

Conclusions:

  • The developed local Buckley-James estimator provides a valuable tool for survival analysis when errors are heteroscedastic.
  • This method enhances the interpretability and accuracy of accelerated failure time models in practical applications.
  • The approach offers a statistically sound and computationally feasible solution for analyzing complex survival data.