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

309
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.
309
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

905
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...
905
Hazard Rate01:11

Hazard Rate

332
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...
332
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

630
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...
630
Relative Risk01:12

Relative Risk

1.5K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
1.5K
Censoring Survival Data01:09

Censoring Survival Data

437
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...
437

You might also read

Related Articles

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

Sort by
Same author

Oncologic outcomes and predictors of recurrence following minimally invasive and open surgery for thymoma.

Journal of thoracic disease·2025
Same author

Non-Parametric Estimation for Semi-Competing Risks Data With Event Misascertainment.

Statistics in medicine·2025
Same author

Estimating optimal individualized treatment rules with multistate processes.

Biometrics·2023
Same author

A reference-free R-learner for treatment recommendation.

Statistical methods in medical research·2022
Same author

Jointly modelling multiple transplant outcomes by a competing risk model via functional principal component analysis.

Journal of applied statistics·2022
Same author

COVID-19 Diagnosis and Risk of Death Among Adults With Cancer in Indiana: Retrospective Cohort Study.

JMIR cancer·2022

Related Experiment Video

Updated: Dec 17, 2025

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

10.7K

On shared gamma-frailty conditional Markov model for semicompeting risks data.

Jing Li1, Ying Zhang2, Giorgos Bakoyannis1

  • 1Department of Biostatistics, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA.

Statistics in Medicine
|June 23, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a practical guideline for analyzing semicompeting risks data using the shared gamma-frailty conditional Markov model (GFCMM). It addresses potential estimation bias in the unrestricted GFCMM, offering a score test and graphical method for model selection.

Keywords:
EM-algorithmMarkov modeldementiafrailtyillness-death modelnonparametricsemicompeting risks

More Related Videos

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

2.4K
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

10.9K

Related Experiment Videos

Last Updated: Dec 17, 2025

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

10.7K
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

2.4K
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

10.9K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Semicompeting risks data blend competing risks and progressive states, presenting unique analytical challenges.
  • The shared gamma-frailty conditional Markov model (GFCMM) is a flexible tool for analyzing such data, with restricted and unrestricted versions.
  • Existing maximum likelihood estimation methods for GFCMM can lead to nonparametric bias in the unrestricted model.

Purpose of the Study:

  • To provide a practical guideline for the appropriate application of the GFCMM in semicompeting risks data analysis.
  • To develop methods for assessing the validity of the restricted GFCMM and detecting bias in the unrestricted GFCMM.
  • To illustrate the application of the guideline using real-world dementia and mortality data.

Main Methods:

  • Development of a score test to evaluate the reasonableness of the restricted GFCMM under proportional hazards assumptions.
  • Introduction of a graphical illustration to detect substantial nonparametric estimation bias in the unrestricted GFCMM when the score test is significant.
  • Application of the proposed guideline to the Indianapolis-Ibadan Dementia Project data.

Main Results:

  • The unrestricted GFCMM can produce nonparametric biased estimation in certain scenarios.
  • The proposed score test and graphical method offer a practical approach to model selection and bias detection.
  • The analysis of dementia data demonstrated how dementia occurrence influences mortality risk.

Conclusions:

  • The developed guideline enhances the reliability of GFCMM application in semicompeting risks analysis.
  • Researchers can use these methods to ensure accurate estimation and valid conclusions from their data.
  • The study provides valuable insights into the interplay between dementia and mortality risk.