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

706
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...
706
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

503
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.
503
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

734
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...
734
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

963
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...
963
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

807
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,...
807

You might also read

Related Articles

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

Sort by
Same author

Sulfur-Substituted SAMs Induce Pb─S Antibonding Hybridization for Efficient and Durable Perovskite-Silicon Tandems.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Diagnostic efficacy of circulating tumor cell in clinically significant prostate cancer.

Scientific reports·2026
Same author

[Embryonic Stem Cell-Derived Mesenchymal Stromal Cell Exosomes Protect Against Radiation-Induced Lymphocyte Injury and Its Related Mechanisms].

Zhongguo shi yan xue ye xue za zhi·2026
Same author

HOPX is required for the generation of umbilical cord blood-derived memory-like NK cells induced by three cytokines.

Frontiers in immunology·2026
Same author

Research on the Determination Method of Additional Safety Factor Margin for Ultradeep Well Pipe Strings under Multisource Loads.

ACS omega·2026
Same author

Machine Learning Accelerated Non-Adiabatic Molecular Dynamics Elucidates Local Polarization Effects on Non-radiative Recombination in Halide Perovskites.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
Same journal

Subgroup Analysis of Interval-censored Failure Time Data With Application to Alzheimer's Disease.

Statistics in medicine·2026
Same journal

Rejoinder to Commentaries on "A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events".

Statistics in medicine·2026
Same journal

A Multi-Stage Drop-the-Loser Design With Superiority Boundaries.

Statistics in medicine·2026
Same journal

Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced Adaptive Sampling.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: May 7, 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

2.9K

Bayesian random effects selection in mixed accelerated failure time model for interval-censored data.

Nusrat Harun1, Bo Cai

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, TX 77030, U.S.A.

Statistics in Medicine
|October 15, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian approach for selecting random effects in mixed-effects accelerated failure time (AFT) models with interval-censored data. The method enhances model parsimony and predictive accuracy for time-to-event analyses.

Keywords:
Cholesky decompositionDirichlet process priorsGaussian mixturesaccelerated failure time modelscorrelated datavariable selection

More Related Videos

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

813
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

9.9K

Related Experiment Videos

Last Updated: May 7, 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

2.9K
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

813
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

9.9K

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Survival Analysis

Background:

  • Medical research often involves time-to-event data with observations at regular intervals, known as interval-censored data.
  • Developing parsimonious models is crucial for enhanced predictive power and interpretability in such analyses.
  • Variable and random effects selection are critical challenges in clustered interval-censored data.

Purpose of the Study:

  • To propose a Bayesian method for random effects selection in mixed-effects accelerated failure time (AFT) models.
  • To address the need for parsimonious and interpretable models in the analysis of interval-censored time-to-event data.
  • To improve predictive accuracy in complex medical datasets.

Main Methods:

  • Utilized a Bayesian framework for random effects selection.
  • Employed Cholesky decomposition of the random effects covariance matrix and parameter-expansion for selection.
  • Incorporated Dirichlet priors for random effects uncertainty and Gaussian mixture for flexible error distribution in AFT models.

Main Results:

  • The proposed Bayesian method effectively performs random effects selection in mixed-effects AFT models.
  • The use of Gaussian mixture for error distribution allows for flexible survival and hazard function prediction.
  • Demonstrated model performance through extensive simulations and application to the Signal Tandmobiel Study(®).

Conclusions:

  • The developed Bayesian approach offers a robust method for variable and random effects selection in interval-censored AFT models.
  • This method enhances model interpretability and predictive performance for time-to-event data.
  • The approach is applicable to complex clustered medical data, aiding in better understanding of event occurrences.