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

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 squares (OLS)...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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 observed.
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

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

Parametric Survival Analysis: Weibull and Exponential Methods

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...

You might also read

Related Articles

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

Sort by
Same author

A New Logistic Model With Subject-Specific and Serially Correlated Time-Specific Distribution-Free Random Effects on the Unit Interval for Longitudinal Binary Data.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

Forecasting waved daily COVID-19 death count series with a novel combination of segmented Poisson model and ARIMA models.

Journal of applied statistics·2023
Same author

Analysis of Longitudinal Binomial Data with Positive Association between the Number of Successes and the Number of Failures: An Application to Stock Instability Study.

Entropy (Basel, Switzerland)·2023
Same author

Tweedie Compound Poisson Models with Covariate-Dependent Random Effects for Multilevel Semicontinuous Data.

Entropy (Basel, Switzerland)·2023
Same author

Regressive Class Modelling for Predicting Trajectories of COVID-19 Fatalities Using Statistical and Machine Learning Models.

Bulletin of the Malaysian Mathematical Sciences Society·2022
Same author

Linking dynamic patterns of COVID-19 spreads in Italy with regional characteristics: a two level longitudinal modelling approach.

Mathematical biosciences and engineering : MBE·2021

Related Experiment Video

Updated: Jun 17, 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

Pattern-mixture zero-inflated mixed models for longitudinal unbalanced count data with excessive zeros.

M Tariqul Hasan1, Gary Sneddon, Renjun Ma

  • 1Department of Mathematics and Statistics, University of New Brunswick, Fredericton, NB, E3B 5A3, Canada. thasan@unb.ca

Biometrical Journal. Biometrische Zeitschrift
|December 24, 2009
PubMed
Summary

This study introduces a new statistical model to analyze unbalanced longitudinal count data, specifically addressing excessive zeros and dropouts common in health studies like skin cancer research.

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

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Related Experiment Videos

Last Updated: Jun 17, 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

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

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Longitudinal studies often feature excessive zero counts and participant dropouts, complicating data analysis.
  • Existing methods for analyzing longitudinal data with zeros typically assume balanced study designs.
  • Participant attrition (dropout) introduces missing data, requiring careful consideration of the underlying missingness mechanism.

Purpose of the Study:

  • To develop a statistical approach for analyzing unbalanced longitudinal count data with both excessive zeros and dropouts.
  • To address the limitations of existing methods that primarily focus on balanced longitudinal data.
  • To analyze longitudinal skin cancer count data, which exhibits significant zero inflation and dropout rates.

Main Methods:

  • Proposed a pattern-mixture zero-inflated model incorporating compound Poisson random effects.
  • Included an autoregressive of order 1 (AR(1)) correlation structure to account for longitudinal dependencies in count responses.
  • Employed a quasi-likelihood approach for model parameter estimation.

Main Results:

  • The developed model effectively handles excessive zeros (83% of observations) and substantial dropout rates (52% of observations) in the skin cancer dataset.
  • The pattern-mixture zero-inflated model with compound Poisson random effects and AR(1) structure provided a robust framework for analysis.
  • The quasi-likelihood estimation method proved suitable for the proposed complex model.

Conclusions:

  • The proposed statistical model offers a viable solution for analyzing unbalanced longitudinal count data with complex zero-inflation and dropout patterns.
  • This methodology is particularly relevant for epidemiological studies, such as the skin cancer prevention study, where such data characteristics are prevalent.
  • The approach enhances the ability to draw valid inferences from real-world longitudinal health data.