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

Semiparametric estimation of random effects using the Cox model based on the EM algorithm.

J P Klein1

  • 1Department of Statistics, Ohio State University, Columbus 43210.

Biometrics
|September 1, 1992
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

[Internet-based self-management interventions : Quality criteria for their use in prevention and treatment of mental disorders].

Der Nervenarzt·2018
Same author

Neurocognitive deficits in schizophrenia. Are we making mountains out of molehills?

Psychological medicine·2017
Same author

Reply to: Comments on 'Intraoperative near-infrared fluorescence imaging using indocyanine green in colorectal carcinomatosis surgery: Proof of concept'.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology·2016
Same author

[Internet-based interventions in the treatment of mental disorders : Overview, quality criteria, perspectives].

Der Nervenarzt·2016
Same author

Intraoperative Near-Infrared Fluorescence Imaging using indocyanine green in colorectal carcinomatosis surgery: Proof of concept.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology·2016
Same author

[Guideline-adherent inpatient psychiatric psychotherapeutic treatment of obsessive-compulsive disorder : Normative definition of personnel requirements].

Der Nervenarzt·2016

This study introduces a statistical method to account for shared unobservable factors (frailty) in survival data. The approach helps better understand risks like smoking and cholesterol by adjusting for these hidden influences.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Survival experiments often involve individuals with shared, unobservable characteristics (frailty).
  • Frailty can stem from genetic, early environmental, or household effects, influencing hazard rates multiplicatively.
  • Existing models may not fully account for these random effects.

Purpose of the Study:

  • To develop and apply a statistical method for survival analysis that incorporates random frailty.
  • To estimate both fixed and random effects in the presence of unobservable shared factors.
  • To examine the impact of smoking and cholesterol on health outcomes while adjusting for frailty.

Main Methods:

  • Utilized the Cox proportional hazards model, accounting for a multiplicative random frailty effect.

Related Experiment Videos

  • Assumed random frailties follow a gamma distribution.
  • Employed an Expectation-Maximization (EM) algorithm with a profile likelihood construction for parameter estimation.

    • Main Results:

    • The developed method successfully estimates fixed and random effects in survival data with frailty.
    • Application to the Framingham Heart Study provided adjusted risk estimates for smoking and cholesterol.
    • Demonstrated the importance of accounting for unobservable shared factors in epidemiological studies.

    Conclusions:

    • The proposed statistical approach effectively handles random frailty in survival analysis.
    • Adjusting for frailty offers a more accurate assessment of risk factors like smoking and cholesterol.
    • This method enhances the understanding of disease etiology by accounting for unobserved heterogeneity.