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

Predictive model selection for repeated measures random effects models using Bayes factors

R E Weiss1, Y Wang, J G Ibrahim

  • 1Department of Biostatistics, UCLA School of Public Health 90095-1772, USA.

Biometrics
|June 1, 1997
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

Discussion of "Optimal test procedures for multiple hypotheses controlling the familywise expected loss" by Willi Maurer, Frank Bretz, and Xiaolei Xun.

Biometrics·2023
Same author

Non-fatal suicidal behaviour, depression and poverty among young men living in low-resource communities in South Africa.

BMC public health·2018
Same author

A novel mutation in the TG gene (G2322S) causing congenital hypothyroidism in a Sudanese family: a case report.

BMC medical genetics·2018
Same author

Patients' understanding of how genotype variation affects benefits of tamoxifen therapy for breast cancer.

Public health genomics·2014
Same author

Pattern of dermatoses in Iraqi children.

Eastern Mediterranean health journal = La revue de sante de la Mediterranee orientale = al-Majallah al-sihhiyah li-sharq al-mutawassit·2012
Same author

Anal human papillomavirus infection in a street-based sample of drug using HIV-positive men.

International journal of STD & AIDS·2012
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
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study introduces a novel predictive approach for Bayesian model selection in repeated measures random effects models. This method simplifies prior specification for fixed effects selection in biostatistical analysis.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Bayesian Analysis

Background:

  • Repeated measures random effects models are common in biostatistics.
  • Model selection tools are limited for complex models beyond generalized linear models.
  • Bayes factors are standard for Bayesian model exploration.

Purpose of the Study:

  • To develop a predictive approach for specifying priors in repeated measures random effects models.
  • To facilitate the selection of fixed effects within these models.
  • To provide a unified prior specification for multiple models.

Main Methods:

  • A predictive approach is proposed for Bayesian model selection.
  • Priors are specified using a single predictive distribution.

Related Experiment Videos

  • Focus is on selecting fixed effects in random effects models.
  • Methodology applied to pediatric pain data.

    • Main Results:

    • The predictive approach offers a unified method for prior specification.
    • It simplifies the selection of fixed effects in complex models.
    • Demonstrated utility in a real-world biostatistical application.

    Conclusions:

    • The developed predictive approach enhances Bayesian model selection for repeated measures data.
    • It provides a practical tool for biostatisticians, particularly for fixed effects selection.
    • The method is robust and applicable to various complex statistical models.