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

1.3K
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...
1.3K
Binomial Probability Distribution01:15

Binomial Probability Distribution

13.1K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
13.1K
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

1.2K
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
1.2K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

707
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...
707
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
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...
359
Survival Tree01:19

Survival Tree

498
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
498

You might also read

Related Articles

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

Sort by
Same author

Substrate and target selectivity of 4'-fluoroadenosine against viral and host polymerases.

The Journal of biological chemistry·2026
Same author

Substrate and target selectivity of 4'-fluoroadenosine against viral and host polymerases.

bioRxiv : the preprint server for biology·2026
Same author

The Q226H Mutation in Avian H5N1 Hemagglutinin Mediates a Path towards Structural Adaptation in Humans.

bioRxiv : the preprint server for biology·2026
Same author

Analysis of Stepped-Wedge Cluster Randomized Trials: A Tutorial Using Marginal Models.

Statistics in medicine·2026
Same author

Preferential remdesivir triphosphate incorporation by SARS-CoV-2 polymerase is altered to ATP by the S759A mutation.

Communications biology·2026
Same author

A Pharmacist Consultant Service for Deprescribing Opioids and Benzodiazepines in Older Adults: A Cluster Randomized Trial.

JAMA network open·2026

Related Experiment Video

Updated: Apr 29, 2026

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

9.9K

Logistic regression for dichotomized counts.

John S Preisser1, Kalyan Das2, Habtamu Benecha3

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA jpreisse@bios.unc.edu.

Statistical Methods in Medical Research
|May 28, 2014
PubMed
Summary

This study introduces a shared-parameter hurdle model for analyzing count data with many zeros, improving efficiency over ordinary logistic regression for dichotomized outcomes. The model enhances estimation of covariate effects on the binary outcome.

Keywords:
binary datadental cariesexcess zeroshurdle modelzero-altered Poisson regressionzero-inflation

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

880
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

14.3K

Related Experiment Videos

Last Updated: Apr 29, 2026

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

9.9K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

880
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

14.3K

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Epidemiology

Background:

  • Dichotomizing count data (zero vs. positive) can lead to information loss and reduced statistical efficiency.
  • Ordinary logistic regression may be suboptimal when applied to such dichotomized count data, especially with a high prevalence of zero counts.

Purpose of the Study:

  • To investigate a shared-parameter hurdle model for more efficient estimation of regression parameters in dichotomized count data.
  • To evaluate the asymptotic efficiency of the hurdle model compared to ordinary logistic regression.
  • To assess the performance of the hurdle model in terms of statistical power and Type I error rates.

Main Methods:

  • A shared-parameter hurdle model was developed, comprising a logistic regression for the dichotomous outcome and an ancillary model for the count process (Poisson or negative binomial).
  • Asymptotic efficiency of the hurdle model's logistic component was compared to ordinary logistic regression.
  • Monte Carlo simulations were conducted to evaluate power and Type I error under various model specifications.

Main Results:

  • The shared-parameter hurdle model demonstrated potential for more efficient estimation of regression parameters compared to ordinary logistic regression.
  • Simulation results provided insights into the power and Type I error characteristics of the proposed model.
  • The model was successfully applied to analyze dental caries data from a randomized clinical trial.

Conclusions:

  • The shared-parameter hurdle model offers an efficient approach for analyzing dichotomized count data with excess zeros.
  • This methodology provides a robust framework for estimating covariate effects in such scenarios.
  • The application to dental caries data highlights the practical utility of the model in public health research.