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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

111
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.
111
Censoring Survival Data01:09

Censoring Survival Data

72
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...
72
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

390
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...
390
Hazard Rate01:11

Hazard Rate

95
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...
95
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

197
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...
197
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

162
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...
162

You might also read

Related Articles

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

Sort by
Same author

A Nested Copula Model for Recurrent Gap Times With a Dependent Terminal Event.

Statistics in medicine·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
Same author

Revealing growth performance in weaned Tibetan pigs along with gut microbial diversity and oxidative status under the impact of ambient temperature.

BMC microbiology·2026
Same author

Biaxial strain and electric field modulated band alignment and optical properties of the MoTe<sub>2</sub>/SnC van der Waals heterostructure.

Journal of physics. Condensed matter : an Institute of Physics journal·2026
Same author

Pulsed field ablation for ventricular arrhythmias: From mechanistic foundations to clinical translation.

Heart rhythm O2·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Jun 15, 2025

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

2.0K

Factor-augmented transformation models for interval-censored failure time data.

Hongxi Li1, Shuwei Li1, Liuquan Sun2

  • 1School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China.

Biometrics
|August 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing interval-censored failure time data with multiple correlated variables. The method effectively reduces dimensionality and avoids multicollinearity, improving analysis accuracy.

Keywords:
expectation-maximization algorithmfactor analysisinterval censoringjoint modelnonparametric maximum likelihood estimation

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

10.1K
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

10.7K

Related Experiment Videos

Last Updated: Jun 15, 2025

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

2.0K
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

10.1K
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

10.7K

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Data Analysis

Background:

  • Interval-censored failure time data are common in research, where exact event times are unknown.
  • Multiple correlated covariates can cause multicollinearity, complicating statistical analyses.
  • Existing methods may struggle with both interval censoring and high-dimensional correlated predictors.

Purpose of the Study:

  • To propose a novel factor-augmented transformation model for interval-censored failure time data.
  • To address challenges of dimensionality reduction and multicollinearity in complex datasets.
  • To provide a robust statistical framework for analyzing time-to-event data with correlated predictors.

Main Methods:

  • Developed a joint modeling framework combining factor analysis and semiparametric transformation models.
  • Employed a factor analysis model to group correlated variables into latent factors.
  • Utilized a nonparametric maximum likelihood estimation with an expectation-maximization algorithm for implementation.

Main Results:

  • The proposed factor-augmented transformation model effectively handles interval-censored data.
  • The method successfully reduces dimensionality and mitigates multicollinearity issues.
  • Asymptotic properties of estimators were established, and simulation studies confirmed empirical performance.

Conclusions:

  • The factor-augmented transformation model offers a powerful approach for analyzing complex failure time data.
  • The method is applicable to real-world studies, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI).
  • An R package (ICTransCFA) is available for practical application of the proposed methodology.