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

Bayesian discrimination with longitudinal data.

P J Brown1, M G Kenward, E E Bassett

  • 1University of Kent at Canterbury, Institute of Mathematics and Statistics, Cornwallis Building, Canterbury, CT2 7NF, UK. Philip.J.Brown@ubc.ac.uk

Biostatistics (Oxford, England)
|August 23, 2003
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

The complex circumstellar environment of supernova 2023ixf.

Nature·2024
Same author

Multi-observer concordance and accuracy of the British Thoracic Society scale and other visual assessment qualitative criteria for solid pulmonary nodule assessment using FDG PET-CT.

Clinical radiology·2020
Same author

Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT.

European journal of nuclear medicine and molecular imaging·2019
Same author

Radiologist and multidisciplinary team clinician opinions on the quality of MRI rectal cancer staging reports: how are we doing?

Clinical radiology·2019
Same author

Current concepts in imaging for local staging of advanced rectal cancer.

Clinical radiology·2019
Same author

Use of bioengineered human commensal gut bacteria-derived microvesicles for mucosal plague vaccine delivery and immunization.

Clinical and experimental immunology·2019
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

This study introduces a new Bayesian method to detect growth hormone doping in athletes using longitudinal biomarker data. The approach accurately identifies individuals receiving growth hormone versus placebo.

Area of Science:

  • Sports Science
  • Biostatistics
  • Analytical Chemistry

Background:

  • Growth hormone abuse in sports poses a significant anti-doping challenge.
  • Accurate detection methods are crucial for maintaining fair competition.
  • Previous methods may lack the sensitivity to detect subtle changes over time.

Purpose of the Study:

  • To develop a novel Bayesian discrimination method for multivariate longitudinal data.
  • To identify individuals using growth hormone (GH) in a sports context.
  • To estimate the effect of GH administration at different dose levels.

Main Methods:

  • A double-blind, placebo-controlled clinical trial involving growth hormone administration.
  • Collection of longitudinal biomarker data (8 markers at 7 time points).

Related Experiment Videos

  • Application of a new Bayesian discrimination model with a Kronecker product covariance structure, estimated via an empirical Bayes approach and ECM algorithm.

    • Main Results:

    • The developed method provides probabilities of an individual being on placebo or one of two GH dose regimes.
    • The model effectively handles multivariate longitudinal data structures.
    • Demonstrated utility in distinguishing between treatment and placebo groups based on biomarker profiles.

    Conclusions:

    • The new Bayesian discrimination method is effective for detecting growth hormone doping.
    • The approach offers a robust statistical framework for analyzing complex biomarker data in anti-doping.
    • This methodology can be applied to future scenarios with varying numbers of markers and time points.