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

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

422
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...
422
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

681
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...
681
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

321
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...
321
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

204
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.
204
Survival Tree01:19

Survival Tree

172
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...
172
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

319
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
319

You might also read

Related Articles

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

Sort by
Same author

Prognostic Value of Skeletal Muscle Loss in Unresectable Hepatocellular Carcinoma Treated with TACE-Based Combination Therapy.

Journal of clinical medicine·2026
Same author

METTL3/FOSL1-associated inflammatory and autophagy-related responses involve m6A-related regulation in sepsis-induced acute kidney injury.

Archives of biochemistry and biophysics·2026
Same author

SpyCatcher-Engineered Ferritin Nanocages Enable Dual-Receptor Targeting for Enhanced Glioma Therapy.

Bioconjugate chemistry·2026
Same author

Fe<sup>3+</sup>-responsive aggregable gold nanoplatform enables siRNA-mediated chemoresistance reversal and combined photothermal-temozolomide therapy for glioblastoma.

Acta biomaterialia·2026
Same author

Interpretable Deep Regression Models With Interval-Censored Failure Time Data.

Statistics in medicine·2026
Same author

Mixed membership latent variable model with unknown factors, factor loadings and number of extreme profiles.

Biometrics·2026

Related Experiment Video

Updated: Sep 28, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K

Joint analysis of multivariate failure time data with latent variables.

Deng Pan1, Xinyuan Song2, Junhao Pan3

  • 1School of Mathematics and Statistics, 12443Huazhong University of Science and Technology, Wuhan, China.

Statistical Methods in Medical Research
|April 4, 2022
PubMed
Summary

This study introduces a novel joint model to analyze observed and latent risk factors influencing multiple failure times. The method effectively identifies key factors in complex health outcomes like diabetic complications.

Keywords:
Multivariate additive hazardsborrow-strength estimationdistribution-free factor analysisgeneralized least squarelatent variablesstructural equation

More Related Videos

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

3.4K
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

10.8K

Related Experiment Videos

Last Updated: Sep 28, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K
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

3.4K
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

10.8K

Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Multivariate failure time data often involves complex relationships between observed and unobserved risk factors.
  • Existing models may not adequately capture the interplay between latent variables and time-to-event outcomes.
  • Understanding these factors is crucial for predicting and managing diseases with multiple complications.

Purpose of the Study:

  • To propose a joint modeling approach for investigating observed and latent risk factors of multivariate failure times.
  • To develop a robust statistical framework that integrates confirmatory factor analysis with multivariate additive hazards models.
  • To provide a method for estimating model parameters and assessing their asymptotic properties.

Main Methods:

  • A distribution-free confirmatory factor analysis (CFA) model to characterize latent factors using observed variables.
  • A multivariate additive hazards model to assess the impact of both observed and latent risk factors on failure times.
  • A hybrid estimation procedure combining borrow-strength and asymptotically distribution-free generalized least squares (ADF-GLS).

Main Results:

  • The proposed hybrid estimation procedure effectively estimates model parameters.
  • Asymptotic properties of the estimators are theoretically derived.
  • Simulation studies confirm the method's good performance in practical scenarios.

Conclusions:

  • The joint modeling approach provides a powerful tool for analyzing multivariate failure times with latent structures.
  • The method is applicable to various fields, including the study of chronic diseases like diabetes.
  • This approach enhances the understanding of complex risk factor interactions in health research.