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

Autoregressive models for describing non-linear changes in biological parameters fitted using BUGS.

A P Mander1, M D Hughes, S J Sharp

  • 1MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, CB2 2SR, U.K.

Statistics in Medicine
|October 16, 1999
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

Intervention to optimise body mass index in adolescents and address the triple burden of malnutrition-the Ntshembo (Hope) trial in rural and urban South Africa study: a study protocol for a randomised controlled trial.

Trials·2026
Same author

Complement factor I: Regulatory nexus, driver of immunopathology, and therapeutic.

Immunobiology·2023
Same author

Experiences that matter in bipolar disorder: a qualitative study using the capability, comfort and calm framework.

International journal of bipolar disorders·2023
Same author

An investigation of factors affecting changes in health behaviours during the COVID-19 pandemic in a UK population-based cohort study.

Public health·2022
Same author

Validity and severity thresholds for the depression subscale of the affective self rating scale: An equipercentile equating study using classical test theory.

Journal of affective disorders·2021
Same author

Correction: External validation of risk prediction models for incident colorectal cancer using UK Biobank.

British journal of cancer·2020

This study introduces autoregressive models to analyze biological data with time-based asymptotic curves. A Bayesian approach using BUGS software models individual and group variations in these biological processes.

Area of Science:

  • Biostatistics
  • Pharmacokinetics
  • Physiological modeling

Background:

  • Biological processes often exhibit time-dependent outcomes that plateau over time.
  • Understanding these asymptotic patterns is crucial for accurate biological modeling.
  • Existing methods may not fully capture individual variability in these processes.

Purpose of the Study:

  • To describe curvilinear associations with time tending to an asymptote using autoregressive models.
  • To present a Bayesian hierarchical modeling approach for analyzing such data.
  • To illustrate the application of these statistical methods using clinical trial data.

Main Methods:

  • Autoregressive models were employed to capture time-dependent associations within subjects.
  • A Bayesian approach, implemented in BUGS, was used for multi-level (hierarchical) modeling.

Related Experiment Videos

  • Peak expiratory flow data from an asthma clinical trial served as a case study.

    • Main Results:

    • Autoregressive models effectively described individual subject data showing asymptotic trends.
    • The Bayesian hierarchical model successfully accounted for inter-subject variation in these trends.
    • The methods provided a robust framework for analyzing complex biological time-series data.

    Conclusions:

    • Autoregressive and Bayesian hierarchical models offer a powerful framework for analyzing biological data with asymptotic time courses.
    • These methods can effectively model both within-subject and between-subject variability.
    • The approach is applicable to various biological and clinical datasets, including peak expiratory flow in asthma.