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

Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

141
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...
141
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

157
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...
157
Law of Independent Assortment02:03

Law of Independent Assortment

61.1K
While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
61.1K
Bar Graph01:07

Bar Graph

20.8K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
20.8K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

373
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
373
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

935
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...
935

You might also read

Related Articles

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

Sort by
Same author

Separability and identifiability as primary obstacles to substantively useful mediation analysis.

European journal of epidemiology·2026
Same author

Assessing Racial Disparities in Healthcare Expenditures via Mediator Distribution Shifts.

Statistics in medicine·2026
Same author

Development and Validation of Machine Learning Models to Identify Emergency Department Patients at Increased Risk of New or Progressive Acute Kidney Injury.

Journal of the American College of Emergency Physicians open·2026
Same author

Target trial emulation without matching: a more efficient approach for evaluating vaccine effectiveness using observational data.

Epidemiology (Cambridge, Mass.)·2026
Same author

Best practices for moving from correlation to causation in ecological research.

Nature communications·2026
Same author

Reducing home infusion CLABSI through a dashboard and toolkit implementation.

Infection control and hospital epidemiology·2026

Related Experiment Video

Updated: Nov 27, 2025

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

2.8K

Full Law Identification in Graphical Models of Missing Data: Completeness Results.

Razieh Nabi1, Rohit Bhattacharya1, Ilya Shpitser1

  • 1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.

Proceedings of Machine Learning Research
|December 7, 2020
PubMed
Summary

This study provides graphical conditions for identifying full data distributions from incomplete data, addressing missing data and unmeasured confounding. These findings are crucial for unbiased statistical inference across various scientific fields.

More Related Videos

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

14.4K
Spotting Cheetahs: Identifying Individuals by Their Footprints
09:47

Spotting Cheetahs: Identifying Individuals by Their Footprints

Published on: May 1, 2016

15.1K

Related Experiment Videos

Last Updated: Nov 27, 2025

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

2.8K
Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

14.4K
Spotting Cheetahs: Identifying Individuals by Their Footprints
09:47

Spotting Cheetahs: Identifying Individuals by Their Footprints

Published on: May 1, 2016

15.1K

Area of Science:

  • Statistics
  • Causal Inference
  • Data Science

Background:

  • Missing data can significantly bias analyses in healthcare, economics, and social sciences.
  • Unbiased inference methods often require specifying missingness as a probability distribution within a directed acyclic graph (DAG).

Purpose of the Study:

  • To characterize identifiable models for non-ignorable missing data distributions within DAG frameworks.
  • To establish necessary and sufficient graphical conditions for recovering full data distributions from observed data.

Main Methods:

  • Utilizing directed acyclic graphs (DAGs) to model data distributions and missingness processes.
  • Developing graphical criteria for identifiability of the full data distribution.

Main Results:

  • The study presents the first completeness result for this class of missing data distributions.
  • Identified necessary and sufficient graphical conditions for recovering the full data distribution from observed data.

Conclusions:

  • The established graphical conditions enable unbiased inference even with non-ignorable missing data.
  • The framework is extended to address scenarios involving both missing data and unobserved confounding.