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

Birth Control Methods01:22

Birth Control Methods

6.8K
Vasectomy is a surgical form of male sterilization that involves severing and sealing the vasa deferentia, preventing sperm from mixing with semen during ejaculation. Because a vasectomy does not impact the testes' ability to produce testosterone, hormone levels, libido, and sexual function generally remain unchanged. While vasectomy is highly effective in preventing pregnancy, with a success rate near 99.85%, rare cases of recanalization (spontaneous reconnection) can occur. Although...
6.8K
Steps in the Modeling Process01:14

Steps in the Modeling Process

683
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
683
Organic Compounds03:02

Organic Compounds

57.5K
All living things are formed mostly of carbon compounds called organic compounds. The category of organic compounds includes both natural and synthetic compounds that contain carbon. Although a single, precise definition has yet to be identified by the chemistry community, most agree that a defining trait of organic molecules is the presence of carbon as the principal element, bonded to hydrogen and other carbon atoms. However, some carbon-containing compounds such as carbonates, cyanides, and...
57.5K
Molecules and Compounds02:38

Molecules and Compounds

68.9K
Atoms and Molecules
68.9K
Coordination Compounds and Nomenclature02:54

Coordination Compounds and Nomenclature

26.8K
In most main group element compounds, the valence electrons of the isolated atoms combine to form chemical bonds that satisfy the octet rule. For instance, the four valence electrons of carbon overlap with electrons from four hydrogen atoms to form CH4. The one valence electron leaves sodium and adds to the seven valence electrons of chlorine to form the ionic formula unit NaCl (Figure 1a). Transition metals do not normally bond in this fashion. They primarily form coordinate covalent bonds, a...
26.8K
Elements and Compounds01:27

Elements and Compounds

105.0K
Pure substances consist of only one type of matter. A pure substance can be an element or a compound. An element consists of only one type of atom, while a compound consists of two or more types of atoms held together by a chemical bond.
Elements
Elements are classified as atomic or molecular based on the nature of their basic units. They are unique forms of matter with specific chemical and physical properties that cannot break down into smaller substances by ordinary chemical reactions. There...
105.0K

You might also read

Related Articles

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

Sort by
Same author

Effective sample size: A measure of individual uncertainty in predictions.

Statistics in medicine·2024
Same author

Analysis of survival outcomes in haematopoietic cell transplant studies: Pitfalls and solutions.

Bone marrow transplantation·2022
Same author

Multiple imputation for cause-specific Cox models: Assessing methods for estimation and prediction.

Statistical methods in medical research·2022
Same author

Validation of prediction models in the presence of competing risks: a guide through modern methods.

BMJ (Clinical research ed.)·2022
Same author

Conventional regression analysis and machine learning in prediction of anastomotic leakage and pulmonary complications after esophagogastric cancer surgery.

Journal of surgical oncology·2022
Same author

Prediction Models for Celiac Disease Development in Children From High-Risk Families: Data From the PreventCD Cohort.

Gastroenterology·2022
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Feb 6, 2026

Frailty Assessment in an Aging Mouse Model
06:58

Frailty Assessment in an Aging Mouse Model

Published on: September 23, 2025

607

Dynamic frailty models based on compound birth-death processes.

Hein Putter1, Hans C van Houwelingen2

  • 1Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands h.putter@lumc.nl.

Biostatistics (Oxford, England)
|February 15, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces dynamic frailty models for continuous survival data, extending previous methods. The new models better capture unobserved heterogeneity over time in survival analysis.

Keywords:
Compound birth–death processesDynamic frailty processesStochastic EM algorithmTime-varying frailtiesUnobserved heterogeneity

More Related Videos

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
14:43

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

Published on: January 12, 2018

13.5K
Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
05:53

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

17.1K

Related Experiment Videos

Last Updated: Feb 6, 2026

Frailty Assessment in an Aging Mouse Model
06:58

Frailty Assessment in an Aging Mouse Model

Published on: September 23, 2025

607
A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
14:43

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

Published on: January 12, 2018

13.5K
Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
05:53

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

17.1K

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Frailty models address unobserved heterogeneity in survival analysis by incorporating random effects.
  • Traditional frailty models assume a constant frailty over time, which is a limiting assumption.
  • Time-varying or dynamic frailties are of significant interest for more realistic modeling.

Purpose of the Study:

  • To extend existing auto-correlated frailty models to continuous survival data.
  • To develop a rigorous construction of continuous-time frailty processes.
  • To derive key statistical functions and estimation methods for dynamic frailty models.

Main Methods:

  • Construction of continuous-time frailty processes using compound birth-death processes.
  • Derivation of marginal hazards, survival functions, and bivariate survival functions for mixture models.
  • Application of a (stochastic) expectation-maximization algorithm for parameter estimation.

Main Results:

  • Rigorous framework for dynamic frailty models in continuous survival data.
  • Derivation of marginal and bivariate survival functions and the cross-ratio function.
  • Development of maximum likelihood estimation procedures using EM algorithm.

Conclusions:

  • The proposed dynamic frailty models provide a flexible extension for survival data.
  • The methods allow for modeling time-varying unobserved heterogeneity.
  • The approach is validated through application to a real-world dataset.