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 Experiment Videos

A Monte Carlo method for Bayesian inference in frailty models.

D G Clayton1

  • 1Department of Community Health, University of Leicester, United Kingdom.

Biometrics
|June 1, 1991
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Appropriate use of information on family history of disease in recruitment for linkage analysis studies.

Annals of human genetics·2006
Same author

Age related macular degeneration and sun exposure, iris colour, and skin sensitivity to sunlight.

The British journal of ophthalmology·2005
Same author

Smoking and age related macular degeneration: the number of pack years of cigarette smoking is a major determinant of risk for both geographic atrophy and choroidal neovascularisation.

The British journal of ophthalmology·2005
Same author

Design and analysis of admixture mapping studies.

American journal of human genetics·2004
Same author

Cost-effective analysis of candidate genes using htSNPs: a staged approach.

Genes and immunity·2004
Same author

Testing the possible negative association of type 1 diabetes and atopic disease by analysis of the interleukin 4 receptor gene.

Genes and immunity·2003
Same journal

Acknowledgment of Referees 2025.

Biometrics·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
See all related articles

This study introduces a Bayesian approach for proportional hazards models with unobserved frailties, enhancing analyses in epidemiology and survival data. Monte Carlo methods are used for inference, addressing limitations of previous models.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Epidemiological and prognostic studies often require methods accounting for unobserved covariates or 'frailties'.
  • Existing proportional hazards models with random effects face challenges with elusive asymptotic property proofs and potentially non-quadratic likelihoods.

Purpose of the Study:

  • To present a Bayesian representation of the proportional hazards model incorporating unobserved frailties.
  • To discuss inference methods for this model using Monte Carlo simulations.

Main Methods:

  • Bayesian statistical modeling.
  • Application of Monte Carlo methods for parameter estimation and inference.
  • Extension of the proportional hazards model to include random effects (frailties).

Related Experiment Videos

Main Results:

  • A novel Bayesian framework for analyzing event history data with unobserved frailties is established.
  • The proposed method offers a practical approach to inference where traditional methods face theoretical or computational difficulties.

Conclusions:

  • The Bayesian representation provides a viable alternative for survival data analysis when unobserved heterogeneity is present.
  • Monte Carlo inference facilitates the application of these advanced models in epidemiological and prognostic research.