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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

74
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...
74
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

3.7K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
3.7K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

197
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...
197
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

107
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...
107
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

109
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...
109

You might also read

Related Articles

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

Sort by
Same author

Sex- and stressor-dependent effects of metformin on cardiac fibrosis in plasminogen activator inhibitor-1 deficiency.

Journal of molecular and cellular cardiology·2026
Same author

"No Vax, No Visit": Psychosocial Impacts of Vaccination-Based Visitation Boundaries Among Australian Parents of Newborns.

Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals·2026
Same author

Human atrial extracellular vesicles suppress NLRP3 inflammasome activation and profibrotic signaling in a patient-specific iPSC model of postoperative atrial fibrillation.

Theranostics·2026
Same author

Programmable SUST-Based SR-CHA with Ir-Coordination MOFs Enhanced Emitters for Universal Sensitive Evaluation of Various Herpesviruses.

Analytical chemistry·2026
Same author

Assessing the psychometric properties of the Autism Diagnostic Observation Schedule - Generic (ADOS-G) in a clinical setting in the Chinese mainland.

Molecular autism·2026
Same author

Radiomic analysis of multiple MRI sequences for diagnosing liver cancer vs. focal nodular hyperplasia.

American journal of translational research·2026

Related Experiment Video

Updated: Aug 10, 2025

Modeling Alcohol Consumption in Rodents Using Two-Bottle Choice Home Cage Drinking and Microstructural Analysis
08:45

Modeling Alcohol Consumption in Rodents Using Two-Bottle Choice Home Cage Drinking and Microstructural Analysis

Published on: November 8, 2024

686

Predicting Alcohol Consumption Patterns for Individuals with a User-Friendly Parsimonious Statistical Model.

Wenbin Liang1,2,3, HuiJun Chih4, Tanya Chikritzhs3

  • 1School of Public Health, Fujian Medical University, Fuzhou 350108, China.

International Journal of Environmental Research and Public Health
|February 11, 2023
PubMed
Summary

This study introduces a new computational model to predict heavy alcohol consumption per occasion and risky drinking days. The model accurately forecasts drinking patterns, aiding public health research on alcohol-related health outcomes.

Keywords:
ROC curvealcohol drinkingprediction modellingpredictive accuracyrisky consumption

More Related Videos

The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans
05:40

The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans

Published on: April 28, 2022

3.1K
Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice
07:31

Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice

Published on: January 7, 2019

8.0K

Related Experiment Videos

Last Updated: Aug 10, 2025

Modeling Alcohol Consumption in Rodents Using Two-Bottle Choice Home Cage Drinking and Microstructural Analysis
08:45

Modeling Alcohol Consumption in Rodents Using Two-Bottle Choice Home Cage Drinking and Microstructural Analysis

Published on: November 8, 2024

686
The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans
05:40

The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans

Published on: April 28, 2022

3.1K
Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice
07:31

Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice

Published on: January 7, 2019

8.0K

Area of Science:

  • Public Health
  • Behavioral Science
  • Epidemiology

Background:

  • Traditional alcohol studies often overlook heavy per-occasion drinking, potentially obscuring true health outcome relationships.
  • Accurate assessment of recent drinking occasions is crucial for understanding alcohol-related health issues.

Purpose of the Study:

  • To develop a user-friendly and replicable computational model for predicting alcohol consumption.
  • To estimate an individual's probability of consuming a specific number of drinks (≥2, 3, 4…) per occasion.
  • To predict the number of days with risky alcohol consumption levels over a defined period.

Main Methods:

  • Utilized data from the 2010 and 2011 National Survey on Drug Use and Health (NSDUH).
  • Developed a predictive model using age, gender, usual daily consumption, and recent drinking frequency (past 30 days).
  • Validated the model by comparing predicted outcomes (drinks per occasion, days of risky consumption) with observed values.

Main Results:

  • The model demonstrated high predictive accuracy, with Area Under the ROC Curve values ranging from 0.86 to 0.91 for predicting drinks consumed.
  • Coefficients close to 1 indicated strong agreement between predicted and observed drinking values.
  • The model effectively predicts both the quantity of alcohol consumed on the last drinking occasion and the frequency of risky consumption days.

Conclusions:

  • A straightforward computational modeling approach was successfully developed and validated.
  • This model can be readily adopted by public health and behavioral studies to better assess alcohol consumption patterns.
  • The findings highlight the importance of considering per-occasion drinking in understanding alcohol-related health risks.