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

Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

73.6K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
73.6K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

33
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
33
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

64
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
64
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

433
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
433
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

490
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
490
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

117
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
117

You might also read

Related Articles

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

Sort by
Same author

Relationship between Antiviral Activity against Influenza A Virus Induced by Compound Combinations and Changes in the Physical Properties of Lipid Bilayers.

ACS pharmacology & translational science·2025
Same author

Implementation of a Conditional Latent Diffusion-Based Generative Model to Synthetically Create Unlabeled Histopathological Images.

Bioengineering (Basel, Switzerland)·2025
Same author

Drug Repurposing for Non-Alcoholic Fatty Liver Disease by Analyzing Networks Among Drugs, Diseases, and Genes.

Metabolites·2025
Same author

Investigating Potential Anti-Bacterial Natural Products Based on Ayurvedic Formulae Using Supervised Network Analysis and Machine Learning Approaches.

Pharmaceuticals (Basel, Switzerland)·2025
Same author

Exploring the Trade-Off in the Variational Information Bottleneck for Regression with a Single Training Run.

Entropy (Basel, Switzerland)·2025
Same author

Identifying Potential Natural Antibiotics from Unani Formulas through Machine Learning Approaches.

Antibiotics (Basel, Switzerland)·2024
Same journal

Correction to: Determination of Alkaloids in Mitragyna speciosa (Kratom) Raw Materials and Dietary Supplements by HPLC-UV: Single-Laboratory Validation, First Action 2017.14.

Journal of AOAC International·2026
Same journal

Single-Laboratory Validation of Simultaneous Determination of Aflatoxins in Nutraceuticals following immune-affinity Column Cleanup and Liquid Chromatography Tandem Mass Spectrometry Analysis.

Journal of AOAC International·2026
Same journal

Determination of Bromoform in Seaweed, Oil, and Animal Feed by GC-MS/MS: AOAC Official Method 2026.01, First Action.

Journal of AOAC International·2026
Same journal

ICRF-Assisted Box-Behnken Design and Optimization for Rapid UPLC-PDA Determination of Dorzolamide Hydrochloride and Timolol Maleate in an Ophthalmic Preparation.

Journal of AOAC International·2026
Same journal

Advancing PFAS Analysis Through Scientific Publication.

Journal of AOAC International·2026
Same journal

A Green UPLC-UV Method for L-Ascorbic Acid Determination Based on a Biodegradable Chelating Agent and Synergistic Hydrophobic-Electrostatic Interactions.

Journal of AOAC International·2026
See all related articles

Related Experiment Video

Updated: Jun 15, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Statistical Modeling of Within-Laboratory Precision Using a Hierarchical Bayesian Approach.

Daisuke Miyake1, Shigehiko Kanaya2, Naoaki Ono2

  • 1Department of Management-Planning, Japan Food Research Laboratories, Motoyoyogi-cho 52-1, Shibuya-ku, Tokyo 151-0062, Japan.

Journal of AOAC International
|August 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a regression method using hierarchical Bayesian modeling to predict within-laboratory standard deviations (SDs) from duplicate measurements. The model accurately estimates precision for various analytes, aiding internal quality control and uncertainty assessment.

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.3K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K

Related Experiment Videos

Last Updated: Jun 15, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.3K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K

Area of Science:

  • Analytical Chemistry
  • Statistical Modeling

Background:

  • Reproducibility in food analysis is well-studied, often following the Horwitz curve.
  • Systematic prediction of repeatability and intermediate precision remains under-researched.

Purpose of the Study:

  • To develop a regression method for estimating within-laboratory standard deviations (SDs).
  • To utilize hierarchical Bayesian modeling with duplicate measurement data.

Main Methods:

  • Employed Hamiltonian Monte Carlo (HMC) method using R with Stan.
  • Assumed a Chi-squared distribution for the statistical model.
  • Incorporated nonlinear fixed effects and lognormal random effects in a hierarchical prior structure.

Main Results:

  • Analyzed over 300 instances, showing good model fit, except for moisture (a method-defined analyte).
  • The method is applicable to diverse analytes analyzed via spectroscopy, GC, and HPLC.
  • Estimated precisions generally met Horwitz ratio criteria, with some high-sensitivity detectors yielding lower SDs.

Conclusions:

  • Propose using predicted within-laboratory precision for internal QC and measurement uncertainty estimation, independent of sample matrices.
  • Statistical modeling of duplicate analysis data simplifies precision estimation for laboratory analytical systems.