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

236
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...
236
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

177
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
177
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

286
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...
286
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

264
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,...
264
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

609
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...
609
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101

You might also read

Related Articles

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

Sort by
Same author

Artificial intelligence for predicting interstitial fibrosis and tubular atrophy using diagnostic ultrasound imaging and biomarkers.

BMJ health & care informatics·2025
Same author

Increased Apolipoprotein A1 Expression Correlates with Tumor-Associated Neutrophils and T Lymphocytes in Upper Tract Urothelial Carcinoma.

Current issues in molecular biology·2024
Same author

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity.

Biometrics·2022
Same author

Estimations of the joint distribution of failure time and failure type with dependent truncation.

Biometrics·2018
Same author

Methods for multivariate recurrent event data with measurement error and informative censoring.

Biometrics·2018
Same author

Assessment of hepatic fatty infiltration using dual-energy computed tomography: a phantom study.

Physiological measurement·2014

Related Experiment Video

Updated: Sep 11, 2025

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

Generalized Method of Moments in Analyzing Recurrent Events for Semiparametric Transformation Models With Informative

Yu-Jen Cheng1, Chang-Yu Tsai2

  • 1Institute of Statistics and Data Science, National Tsing Hua University, Taiwan.

Statistics in Medicine
|August 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces flexible semiparametric transformation models for recurrent events, accounting for shared frailty. The novel methods offer robust estimation without strict process or distribution assumptions, enhancing survival analysis.

Keywords:
generalized method of momentsinformative censoringrecurrent event processsemiparametric transformation models

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
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

10.3K

Related Experiment Videos

Last Updated: Sep 11, 2025

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
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
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

10.3K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Recurrent event data analysis often employs shared frailty models to account for correlations.
  • Existing shared-frailty proportional rate models impose restrictive assumptions on rate functions.
  • A need exists for more flexible models that do not assume proportionality over time.

Purpose of the Study:

  • To develop semiparametric transformation models for recurrent events incorporating shared frailty.
  • To allow for non-proportional rate functions between different covariate groups.
  • To propose robust and efficient estimation methods for these models.

Main Methods:

  • Utilized a shared frailty variable to model the correlation between recurrent events and censoring.
  • Decomposed the rate function into shape and size components, inspired by Wang and Huang.
  • Developed an inverse-rate weighting approach and a generalized method of moments framework for estimation.

Main Results:

  • The proposed models accommodate non-proportional rate functions, offering greater flexibility than traditional methods.
  • The generalized method of moments framework improves estimation efficiency by combining component estimators.
  • Established large sample properties and validated finite sample performance through simulations.

Conclusions:

  • The novel semiparametric transformation models provide a robust framework for analyzing recurrent event data with shared frailty.
  • The proposed estimation methods are efficient and do not rely on restrictive assumptions about the event process or frailty distribution.
  • The methods were successfully applied to a real-world dataset, demonstrating practical utility.