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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

318
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...
318
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

681
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
681
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

337
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...
337
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

1.4K
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...
1.4K
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

382
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
382

You might also read

Related Articles

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

Sort by
Same author

Optimizing diabetes self-care in patients with limited health literacy: a randomized controlled trial (RCT) of SCT-based education with and without an AI-designed photo-novel.

BMC health services research·2026
Same author

The effect of web-based educational intervention on caries-preventive oral health behaviors in pregnant women: an application of the health belief model.

BMC public health·2026
Same author

The GLOBE Trial: Efficacy and Safety of L-Glutamine Plus Hydroxyurea Versus Hydroxyurea Alone in Sickle Cell Anemia - A Double-Blind, Randomized Study

Turkish journal of haematology : official journal of Turkish Society of Haematology·2026
Same author

Probabilistic risk assessment of occupational exposure to respirable crystalline silica among ceramic workers in an industrial town in Iran: a Monte Carlo simulation approach.

Scientific reports·2026
Same author

Neurofeedback as an Adjunct to Pharmacotherapy in OCD: A Randomized Controlled Trial on Symptom Reduction.

Applied psychophysiology and biofeedback·2026
Same author

Association between the digestion-resistant bioactive peptide content in dairy products and the risk of sarcopenia in Iranian elderly: a case-control study.

Journal of health, population, and nutrition·2025

Related Experiment Video

Updated: Mar 18, 2026

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

3.8K

Semiparametric models for multilevel overdispersed count data with extra zeros.

Marzieh Mahmoodi1, Abbas Moghimbeigi2, Kazem Mohammad3

  • 11 Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.

Statistical Methods in Medical Research
|July 9, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces advanced statistical models for analyzing complex count data with excess zeros and overdispersion. These semiparametric multilevel methods offer robust analysis for hierarchical data, including dental health research.

Keywords:
Semiparametricmultileveloverdispersionzero inflated generalized Poissonzero inflated negative binomial

More Related Videos

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
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Related Experiment Videos

Last Updated: Mar 18, 2026

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

3.8K
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
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Epidemiology

Background:

  • Hierarchical count data frequently exhibit excess zeros and overdispersion, complicating standard statistical analyses.
  • Existing methods may not adequately capture nonlinear covariate effects in such complex data structures.
  • Accurate modeling is crucial for understanding health outcomes and informing interventions.

Purpose of the Study:

  • To propose and compare novel semiparametric models for hierarchical count data with simultaneous excess zeros and overdispersion.
  • To evaluate the performance of semiparametric multilevel zero-inflated negative binomial and generalized Poisson models.
  • To demonstrate the application of these models using real-world dental health data.

Main Methods:

  • Development of flexible semiparametric multilevel regression techniques to handle nonlinear covariate effects.
  • Implementation of an EM algorithm with Newton-Raphson equations for maximum penalized likelihood estimation.
  • Comprehensive comparison of proposed models using both simulated and real-world datasets.

Main Results:

  • The proposed semiparametric models effectively address both excess zeros and overdispersion in hierarchical count data.
  • Monte Carlo simulations demonstrated the performance and reliability of the developed statistical approaches.
  • The models provided valuable insights into the analysis of decayed, missing, and filled teeth data.

Conclusions:

  • Semiparametric multilevel models offer a powerful and flexible framework for analyzing complex count data.
  • These methods are suitable for various applications, including public health and epidemiological research.
  • The study provides a robust methodology for handling common challenges in count data analysis.