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

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

1.3K
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...
1.3K
Quadratic Models01:23

Quadratic Models

282
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
282
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

476
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
476
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

301
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...
301
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.2K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.3K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.3K

You might also read

Related Articles

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

Sort by
Same author

Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.

Journal of clinical and translational science·2026
Same author

Strategies for mitigating artificial intelligence bias in healthcare: a systematic review.

JAMIA open·2026
Same author

Defining Prenatal Care Surveillance Metrics Using Electronic Health Record Data.

JAMA health forum·2026
Same author

Cardiovascular Disease Risk and Noncardiovascular Chronic Disease Burden by Housing Status.

Journal of the American Heart Association·2026
Same author

Multinational validation of the PREVENT and SCORE2 cardiovascular risk equations across 6.4 million individuals.

Nature medicine·2026
Same author

A Melt-Infused Li-Sn Alloy Anode with an Iodine-Rich Interface for Long-Life and High-Rate Lithium Metal Batteries.

ACS applied materials & interfaces·2026
Same journal

Interim analysis in sequential multiple assignment randomized trials for survival outcomes.

Biometrics·2026
Same journal

Acknowledgment of Referees 2025.

Biometrics·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Mar 11, 2026

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.4K

A semiparametric model for vQTL mapping.

Chuan Hong1, Yang Ning2, Peng Wei3

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A.

Biometrics
|November 19, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical test to find genetic markers affecting both the average and variability of traits. The method enhances the detection of complex genetic influences on quantitative traits.

Keywords:
Composite likelihoodConditional likelihoodExponential tilt modelPseudolikelihoodSemiparametric modelVQTL

More Related Videos

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
11:37

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii

Published on: June 22, 2017

16.9K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Related Experiment Videos

Last Updated: Mar 11, 2026

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.4K
QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
11:37

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii

Published on: June 22, 2017

16.9K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Quantitative trait locus (QTL) analysis typically focuses on mean trait differences.
  • Variability-controlling QTLs, affecting trait variance, are increasingly recognized.
  • Existing methods for unequal variances have limitations in robustness and power.

Purpose of the Study:

  • To develop a novel statistical method for detecting genetic loci with combined mean and variance effects on quantitative traits.
  • To address limitations of existing tests for detecting genetic influences on trait variability.
  • To provide a robust and powerful tool for identifying complex genetic associations.

Main Methods:

  • Proposed a semiparametric model to detect combined differences in higher moments among genotypic groups.
  • Developed a novel pairwise conditional likelihood ratio test, eliminating the need for unknown reference distributions.
  • The test yields a simple asymptotic chi-square distribution, avoiding permutation or bootstrap procedures.

Main Results:

  • Simulation studies demonstrated effective Type I error control and competitive power.
  • The proposed test successfully identified combined mean and variance effects in body mass index data.
  • The method showed robustness to model mis-specifications.

Conclusions:

  • The novel pairwise likelihood ratio test offers a powerful and robust approach for QTL analysis.
  • This method enhances the ability to detect genetic loci influencing both mean and variance of quantitative traits.
  • The findings have implications for understanding complex genetic architectures and identifying disease-related genetic factors.