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

Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

12.0K
The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
12.0K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

109
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
109
Variability: Analysis01:11

Variability: Analysis

162
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
162
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

80
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
80
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

97
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
97
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

99
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...
99

You might also read

Related Articles

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

Sort by
Same author

Route of Allergen Immunotherapy: A Global Look Into Physicians' Motivations.

Allergy·2026
Same author

Methodological guidance on clinical prediction models in mental health research.

Psychological medicine·2026
Same author

Satellite data show trees delay budburst across landscapes to escape herbivores.

Nature ecology & evolution·2026
Same author

Flexible Bayesian modeling of non-equidispersed counts with penalized complexity priors in disease incidence studies.

Statistical methods in medical research·2026
Same author

Glucodensity-Based Models Outperform Time in Range and Glycemia Risk Index in Prediction Models.

Journal of diabetes science and technology·2026
Same author

Impact of specialised endocrinology care on metabolic control and healthcare utilisation outcomes after kidney transplantation in patients with diabetes: A 12-month observational cohort study.

Diabetes, obesity & metabolism·2026

Related Experiment Video

Updated: Jul 25, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.7K

Multivariate reference and tolerance regions based on conditional transformation models: Application to glycemic

Óscar Lado-Baleato1,2, Carmen Cadarso-Suárez3,4, Thomas Kneib5

  • 1Research Methods Group (RESMET), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.

Biometrical Journal. Biometrische Zeitschrift
|June 26, 2023
PubMed
Summary

Multivariate reference regions (MVRs) improve health status determination from multiple diagnostic tests. Novel statistical models (MCTMs) enhance MVR interpretability and covariate adjustment for clinical use.

Keywords:
Bernstein basisdiabetesmultivariate regressionreference regionstransformation analysis

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K
A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli
08:01

A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli

Published on: August 12, 2016

9.0K

Related Experiment Videos

Last Updated: Jul 25, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.7K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K
A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli
08:01

A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli

Published on: August 12, 2016

9.0K

Area of Science:

  • Biostatistics
  • Medical Decision Making
  • Diagnostic Testing

Background:

  • Reference intervals are crucial for medical decision-making, but multivariate reference regions (MVRs) are underutilized due to interpretability and distributional assumptions.
  • Existing methods for multivariate reference regions (MVRs) often assume Gaussian distributions and lack covariate adjustment, limiting their clinical applicability.

Purpose of the Study:

  • To introduce a novel formulation for multivariate reference regions (MVRs) using multivariate conditional transformation models (MCTMs).
  • To address the estimation uncertainty of MVRs by incorporating tolerance regions.
  • To develop MVRs that are interpretable, easily applicable by physicians, and adjusted for covariates.

Main Methods:

  • Development of a new formulation for MVRs based on multivariate conditional transformation models (MCTMs).
  • Inclusion of tolerance regions to account for estimation uncertainty in MVRs.
  • Estimation of covariate effects on joint and marginal distributions of multivariate response variables without parametric restrictions.

Main Results:

  • The proposed MCTM-based conditional MVRs demonstrated reliability with simulated data.
  • The method was successfully applied to a real-world dataset involving two glycemic markers.
  • The approach allows for the estimation of potentially nonlinear covariate effects on diagnostic test results.

Conclusions:

  • The novel MCTM framework provides a flexible and interpretable approach to constructing covariate-adjusted MVRs.
  • This statistical advancement enhances the clinical utility of multivariate diagnostic test interpretation.
  • The developed methods offer a promising solution for overcoming limitations of traditional reference intervals in complex clinical scenarios.