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 Concept Videos

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
Crossover Experiments01:16

Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
Bioequivalence: Overview01:16

Bioequivalence: Overview

Pharmaceutical equivalents, by definition, are drug products with the same active ingredient in the same quantities, encapsulated in identical dosage forms, and intended for the same administration routes. These pharmaceutical equivalents are deemed bioequivalent if the bioavailability of the active entity in the drug preparations is similar. Moreover, pharmaceutical equivalents demonstrating bioequivalence are also regarded as therapeutically equivalent. This means that when used as directed,...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Plasma CXCL13 and fibrosis biomarkers in COVID-19 compared with idiopathic pulmonary fibrosis.

Scientific reports·2026
Same author

Alternative posology for brolucizumab: a 6-week loading regimen is as effective as a 4-week regimen for the treatment of nAMD.

BMJ open ophthalmology·2026
Same author

High Nasopharyngeal SARS-CoV-2 Load and Delayed Clearance in Hospitalized Patients With Blood Autoantibodies Neutralizing Type I Interferons.

The Journal of infectious diseases·2026
Same author

Objective First, Method Second: Why the Estimand Definition Comes First in Pharmacometric Covariate Modeling.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Assessing Covariate Clinical Relevance in High-Dimensional PK Analysis: A Comparison of SCM+, FFEM, and FREM Approaches.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Advances and Further Comparison of Software Tools for Fisher Information Matrix-Based Design Evaluation in Pharmacometrics.

Pharmaceutical research·2026
Same journal

Checking Genetic Homogeneity Between Two Samples Using Summary Statistics With Application to Mendelian Randomization.

Statistics in medicine·2026
Same journal

A Bayesian Learning Model for Joint Risk Prediction of Alcohol and Cannabis Use Disorders.

Statistics in medicine·2026
Same journal

Reluctant Transfer Learning in Penalized Regressions for Individualized Treatment Rules Under Effect Heterogeneity.

Statistics in medicine·2026
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
See all related articles

Related Experiment Video

Updated: May 30, 2026

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
06:18

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR

Published on: July 11, 2025

Model-based analyses of bioequivalence crossover trials using the stochastic approximation expectation maximisation

Anne Dubois1, Marc Lavielle, Sandro Gsteiger

  • 1INSERM UMR738, University Diderot Paris 7, Paris, France. anne.dubois@inserm.fr

Statistics in Medicine
|July 28, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces nonlinear mixed effects models (NLMEM) for bioequivalence analysis, offering an alternative to standard noncompartmental analysis (NCA). NLMEM provides accurate estimates, especially with sparse data, and reliable bioequivalence tests, though caution is advised for small sample sizes.

More Related Videos

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Related Experiment Videos

Last Updated: May 30, 2026

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
06:18

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR

Published on: July 11, 2025

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Area of Science:

  • Pharmacokinetics
  • Statistical Modeling
  • Drug Development

Background:

  • Noncompartmental analysis (NCA) is the standard for bioequivalence testing.
  • NLMEM offers a potentially more robust statistical framework for pharmacokinetic analysis.
  • NLMEM can account for between-subject and within-subject variability more comprehensively.

Purpose of the Study:

  • To develop and evaluate bioequivalence analysis using Nonlinear Mixed Effects Models (NLMEM) as an alternative to NCA.
  • To compare the performance of NLMEM-based and NCA-based bioequivalence tests via simulation.
  • To assess the accuracy of parameter estimates and type I error rates under various simulation settings.

Main Methods:

  • NLMEM parameter estimation using the SAEM algorithm (implemented in Monolix).
  • Development of Wald and Likelihood Ratio Tests for NLMEM-based bioequivalence.
  • Simulation of crossover trials with varying sample sizes, variability, and designs (sparse vs. rich).

Main Results:

  • NLMEM-based geometric means showed accurate estimation with SAEM, unlike biased NCA estimates in sparse designs.
  • NCA bioequivalence tests maintained good type I error rates, except in high variability scenarios.
  • NLMEM bioequivalence tests (Wald, LRT) demonstrated nominal type I error rates in rich designs but were inflated in sparse designs.

Conclusions:

  • NLMEM-based bioequivalence tests are a viable alternative to NCA, particularly for complex pharmacokinetic profiles.
  • NLMEM provides more accurate estimates than NCA in sparse data designs.
  • Caution is recommended when applying NLMEM-based tests with small sample sizes or highly variable drugs.