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 semiparametric Bayesian approach to the random effects model

K P Kleinman1, J G Ibrahim

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.

Biometrics
|September 29, 1998
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

Maternal experiences of racial discrimination and child weight status in the first 3 years of life.

Journal of developmental origins of health and disease·2014
Same author

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

Public health genomics·2014
Same author

Do community-level predictors of pneumococcal carriage continue to play a role in the conjugate vaccine era?

Epidemiology and infection·2013
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

The TBX21 transcription factor T-1993C polymorphism is associated with decreased IFN-γ and IL-4 production by primary human lymphocytes.

Human immunology·2012
Same journal

Statistical analysis of disease onset during lifespan with left truncation.

Biometrics·2026
Same journal

Interim analysis in sequential multiple assignment randomized trials for survival outcomes.

Biometrics·2026
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
See all related articles

This study introduces a flexible Bayesian model for longitudinal data, moving beyond normal distribution assumptions for random effects. This nonparametric approach enhances analysis accuracy for complex health studies.

Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Standard longitudinal random effects models often assume normal distributions for random effects.
  • This assumption can limit the flexibility and accuracy of analyses in complex biological and health studies.

Purpose of the Study:

  • To develop and present a Bayesian longitudinal random effects model that accommodates nonparametric prior distributions for random effects.
  • To offer a more flexible alternative to traditional models with normality assumptions.

Main Methods:

  • Implementation of a Bayesian model utilizing a Dirichlet process prior for the random effects distribution.
  • Employing a Gibbs sampler for computational feasibility of the proposed nonparametric model.

Related Experiment Videos

Main Results:

  • The proposed Dirichlet process prior allows for a flexible, data-driven distribution of random effects.
  • The Gibbs sampler effectively enables computation for this complex Bayesian nonparametric model.

Conclusions:

  • The developed Bayesian nonparametric approach provides a powerful tool for analyzing longitudinal data where random effects distributions may deviate from normality.
  • The methodology is illustrated effectively using marker data from an AIDS study, demonstrating its practical applicability in real-world health research.