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

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

569
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...
569
Poisson Probability Distribution01:09

Poisson Probability Distribution

11.6K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
11.6K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.0K
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...
1.0K
Censoring Survival Data01:09

Censoring Survival Data

516
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
516
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

388
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
388
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

735
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
735

You might also read

Related Articles

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

Sort by
Same author

Semiparametric methods for incomplete longitudinal count data with an application to health and retirement study.

Journal of applied statistics·2022
Same author

Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes.

Lifetime data analysis·2020
Same author

Estimation in generalized linear models under censored covariates with an application to MIREC data.

Statistics in medicine·2018
Same author

Robust estimation in accelerated failure time models.

Lifetime data analysis·2018
Same author

Joint modeling of longitudinal and survival data with a covariate subject to a limit of detection.

Statistical methods in medical research·2017
Same author

Identification of chemical mixtures to which Canadian pregnant women are exposed: The MIREC Study.

Environment international·2017
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: Jan 12, 2026

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

11.0K

Zero-inflated Poisson mixed model for longitudinal count data with informative dropouts.

Sanjoy K Sinha1

  • 1School of Mathematics and Statistics, Carleton University, Ottawa, ON, Canada.

Journal of Applied Statistics
|November 5, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient method for analyzing longitudinal count data with excess zeros and missing values. The approach handles correlations and non-ignorable missingness for accurate statistical inference.

Keywords:
Count datalongitudinal responsemixed modelnonignorable missignesszero-inflated Poisson

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

10.7K
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

3.7K

Related Experiment Videos

Last Updated: Jan 12, 2026

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

11.0K
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

10.7K
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

3.7K

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Longitudinal studies generate correlated count data, often with excess zeros.
  • Standard models may fail with excess zeros and non-ignorable missing data.
  • Zero-inflated Poisson (ZIP) mixed models address excess zeros and correlations.

Purpose of the Study:

  • To propose an efficient statistical method for analyzing longitudinal count data.
  • To address challenges of excess zeros, correlated observations, and non-ignorable missing responses.
  • To provide a robust framework for health study data analysis.

Main Methods:

  • Development of an efficient method for Zero-inflated Poisson (ZIP) mixed models.
  • Incorporation of a missingness mechanism for non-ignorable dropouts.
  • Utilizing Monte Carlo simulations to evaluate estimator properties.

Main Results:

  • The proposed method effectively handles excess zeros and correlations in longitudinal count data.
  • The approach provides valid inference even with non-ignorable missing responses.
  • Simulation studies demonstrate the empirical properties of the estimators.

Conclusions:

  • The developed method offers a robust solution for complex longitudinal count data.
  • This approach is applicable to health studies with missing data and excess zeros.
  • The findings contribute to advanced statistical techniques for correlated and incomplete data.