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 Bayesian analysis of regression models with continuous errors with application to longitudinal studies

L D Broemeling1, P Cook

  • 1University of Texas School of Public Health, El Paso 79968, USA.

Statistics in Medicine
|February 28, 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

Different strategies towards strength: Unveiling the role of Zn vs Mn/Ca and chitin arrangement in scorpion stingers.

Journal of structural biology·2025
Same author

A nonmyeloablative chimeric mouse model accurately defines microglia and macrophage contribution in glioma.

Neuropathology and applied neurobiology·2018
Same author

An inductively coupled plasma mass spectrometry method for relative free copper determination and generation of a paediatric reference interval.

Annals of clinical biochemistry·2017
Same author

Sample contamination as a source of error in trace element analysis: an ever-present threat.

Journal of clinical pathology·2016
Same author

False-normal vitamin B12 results in a patient with pernicious anaemia.

Clinical biochemistry·2015
Same author

An alternative approach to teaching joint disease.

Medical teacher·2014
Same journal

Predictor-Assisted Nonparametric Graphical Models With Multivariate Error-Prone Data.

Statistics in medicine·2026
Same journal

Optimizing Treatment Decision Estimation for Right-Censored Survival Data Through Parameter Transfer Learning.

Statistics in medicine·2026
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
See all related articles

This study introduces a Bayesian regression model for analyzing longitudinal data, even with unevenly spaced observations. The method effectively models foetal head circumference growth over menstrual age, offering a robust alternative to traditional techniques.

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Bayesian Inference

Background:

  • Analyzing longitudinal studies with unequally spaced observations presents statistical challenges.
  • Existing regression models may not adequately handle the complexities of time-series data in biological contexts.

Purpose of the Study:

  • To introduce and illustrate a novel Bayesian regression model for analyzing longitudinal data.
  • To address the challenge of unequally spaced observations in such analyses.
  • To compare the proposed Bayesian approach with maximum likelihood techniques.

Main Methods:

  • Employed a regression model incorporating errors that follow a continuous autoregressive process.
  • Utilized a Bayesian approach with a direct resampling process for posterior distribution inference.

Related Experiment Videos

  • Applied the model to a longitudinal study of foetal head circumference and menstrual age.

    • Main Results:

    • The proposed Bayesian model effectively handles unequally spaced observations in longitudinal studies.
    • Demonstrated the application of Bayesian inference through resampling for parameter estimation.
    • Provided a comparative analysis against maximum likelihood methods using real-world data.

    Conclusions:

    • The developed Bayesian regression model offers a flexible and robust framework for longitudinal data analysis.
    • The resampling-based inference is a practical approach for complex Bayesian models.
    • The study highlights the utility of this method in biomedical research, specifically in tracking foetal development.