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

Regression with frailty in survival analysis.

C A McGilchrist1, C W Aisbett

  • 1University of New South Wales, Kensington, Australia.

Biometrics
|June 1, 1991
PubMed
Summary

This study introduces a statistical model to account for individual heterogeneity, also known as frailty, in survival analysis. The model is applied to repeated measures of event recurrence times, improving the analysis of complex survival data.

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

Use of diagnosis codes to understand variations in hysterectomy rates: a pilot study.

The Medical journal of Australia·2000
Same author

Model selection and population size using capture-recapture methods.

Journal of clinical epidemiology·1999
Same author

ML and REML estimation in survival analysis with time dependent correlated frailty.

Statistics in medicine·1998
Same author

Severe cervical spinal cord injuries related to rugby union and league football in New South Wales, 1984-1996.

The Medical journal of Australia·1998
Same author

Threshold models in a methadone programme evaluation.

Statistics in medicine·1996
Same author

Trends in the prevalence of trachoma, South Australia, 1976 to 1990.

Australian and New Zealand journal of public health·1996

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Survival studies often face challenges due to unobserved individual heterogeneity (frailty) influencing hazard functions.
  • Frailty is a common factor in repeated event recurrence times within individuals.
  • Existing models may not fully capture the impact of unmeasured variables on survival outcomes.

Purpose of the Study:

  • To develop and apply a statistical model that incorporates individual heterogeneity (frailty) into survival analysis.
  • To analyze repeated measures of event recurrence times where frailty is a shared factor.
  • To enhance the accuracy of survival predictions by accounting for unobserved risk factors.

Main Methods:

  • Development of a statistical model incorporating a frailty component.
  • Application of the model to repeated measures of event recurrence times.
  • Estimation and analysis of model parameters to assess the impact of frailty.

Main Results:

  • The fitted model successfully accounts for the common frailty factor in recurrent event data.
  • Individual heterogeneity significantly influences the hazard function in the analyzed datasets.
  • The model provides improved estimates for survival probabilities and recurrence times.

Conclusions:

  • Statistical models incorporating frailty are essential for accurate survival analysis with repeated events.
  • Accounting for individual heterogeneity improves understanding of event recurrence patterns.
  • This approach offers a more robust framework for analyzing complex survival data.

Related Experiment Videos