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

Biostatistics: Overview01:20

Biostatistics: Overview

293
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
293
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

667
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
667
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

68
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...
68
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

252
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...
252
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

437
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
437
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

497
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...
497

You might also read

Related Articles

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

Sort by
Same author

Evaluating Cross-Platform Batch Correction Methods for Integrated Microarray and RNA-seq Data Analysis.

bioRxiv : the preprint server for biology·2026
Same author

Genetic diversity of collaborative cross mice enables the establishment of a novel <i>Chlamydia muridarum</i> female genital tract infection model.

Infection and immunity·2026
Same author

Biomass and valve length of diatoms living on tree bark as novel indicators of atmospheric environment changes.

Environmental research·2025
Same author

Effects of animal-assisted therapy on dental anxiety, behavior, and perceptions in young pediatric patients: a blinded randomized controlled trial.

Trials·2025
Same author

Mouth Care Without a Battle: Change in Assisted Living Staff Self-Efficacy and Attitudes.

Journal of the American Medical Directors Association·2025
Same author

Effects of microbiota-based interventions on depression and anxiety in children and adolescents-A systematic review.

Journal of pediatric gastroenterology and nutrition·2025

Related Experiment Video

Updated: Jul 30, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

A bivariate zero-inflated negative binomial model and its applications to biomedical settings.

Hunyong Cho1, Chuwen Liu1, John S Preisser1

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA.

Statistical Methods in Medical Research
|May 11, 2023
PubMed
Summary

We introduce a bivariate zero-inflated negative binomial model to analyze correlated count data, effectively handling excess zeros and overdispersion in biomedical research. This model offers intuitive interpretations and is implemented in the R package "bzinb".

Keywords:
Bivariate zero-inflated negative binomial modeldental cariesexpectation-maximization algorithmsingle-cell RNA sequencing

More Related Videos

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

10.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

Related Experiment Videos

Last Updated: Jul 30, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K
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

10.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

Area of Science:

  • Biostatistics
  • Computational Biology
  • Epidemiology

Background:

  • The zero-inflated negative binomial (ZINB) distribution is crucial for biomedical count data analysis, addressing excess zeros and overdispersion.
  • Analyzing correlated count variables necessitates bivariate models to capture complex distributional relationships.
  • Existing methods may not adequately address the unique challenges of sparse data like single-cell RNA sequencing (scRNA-seq) or paired count outcomes in dental research.

Purpose of the Study:

  • To develop a flexible and richly parametrized bivariate zero-inflated negative binomial (bzinb) model.
  • To provide intuitive interpretations for model parameters within a simple latent variable framework.
  • To demonstrate the model's utility in analyzing correlated count data from scRNA-seq and dental caries studies.

Main Methods:

  • Development of a novel bivariate zero-inflated negative binomial model with eight free parameters.
  • Utilized a latent variable framework for model construction and parameter estimation.
  • Applied the model to sparse scRNA-seq data to estimate gene correlations adjusted for dropout events and to dental caries data to assess treatment effects.

Main Results:

  • The proposed bzinb model effectively handles excess zeros and overdispersion in correlated count data.
  • In scRNA-seq data, the model accurately estimated gene correlations after accounting for dropout events.
  • Analysis of dental caries data revealed the impact of Xylitol lozenge treatment on marginal means and response patterns across two tooth surfaces.

Conclusions:

  • The developed bivariate zero-inflated negative binomial model provides a robust framework for analyzing complex correlated count data in biomedical research.
  • The model's intuitive parameterization and application to real-world sparse data (scRNA-seq) and paired outcomes (dental caries) highlight its practical value.
  • The availability of the 'bzinb' R package facilitates broader adoption and application of this advanced statistical methodology.