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 modeling of multivariate average bioequivalence.

Pulak Ghosh1, Mithat Gönen

  • 1Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303-3083, USA. pghosh@mathstat.gsu.edu

Statistics in Medicine
|December 21, 2007
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

Value of adding dynamic contrast-enhanced MRI visual assessment to conventional MRI and clinical assessment in the diagnosis of complete tumour response to chemoradiotherapy for rectal cancer.

European radiology·2018
Same author

Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis.

HPB : the official journal of the International Hepato Pancreato Biliary Association·2018
Same author

A perioperative multidisciplinary care bundle reduces surgical site infections in patients undergoing synchronous colorectal and liver resection.

HPB : the official journal of the International Hepato Pancreato Biliary Association·2018
Same author

Clonal diversity predicts adverse outcome in chronic lymphocytic leukemia.

Leukemia·2018
Same author

Is Hepatectomy Justified for BRAF Mutant Colorectal Liver Metastases?: A Multi-institutional Analysis of 1497 Patients.

Annals of surgery·2018
Same author

Hypophosphatemia as a Predictor of Organ-Specific Complications Following Gastrointestinal Surgery: Analysis of 8034 Patients.

World journal of surgery·2018
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

This study introduces a new Bayesian method for multivariate bioequivalence testing, accounting for endpoint correlations. This approach improves drug formulation comparisons by analyzing multiple drug measurements simultaneously.

Area of Science:

  • Pharmacokinetics and Drug Development
  • Biostatistics
  • Bayesian Inference

Background:

  • Bioequivalence trials compare drug formulations, typically using univariate methods.
  • Current practice often ignores correlations between key pharmacokinetic endpoints like AUC and Cmax.
  • Multivariate bioequivalence offers a more comprehensive assessment but lacks standardized methods.

Purpose of the Study:

  • To develop a semiparametric Bayesian test for multivariate bioequivalence.
  • To incorporate and analyze the impact of correlations between multiple drug endpoints.
  • To provide a more robust method for comparing drug formulations.

Main Methods:

  • Developed a semiparametric Bayesian framework for multivariate bioequivalence.
  • Incorporated endpoint correlations into the statistical model.

Related Experiment Videos

  • Utilized prior correlations to inform estimates and posterior probabilities.

    • Main Results:

    • Demonstrated how endpoint correlations influence bioequivalence inference.
    • Showcased the 'borrow strength' phenomenon in estimates and probabilities.
    • Illustrated the method's application with a real-world dataset.

    Conclusions:

    • The proposed Bayesian method offers a statistically sound approach to multivariate bioequivalence.
    • Accounting for endpoint correlations provides more accurate and reliable comparisons of drug formulations.
    • This method enhances the analysis of bioequivalence trials with multiple endpoints.