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

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Binomial Probability Distribution01:15

Binomial Probability Distribution

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,...
Probability Laws01:49

Probability Laws

Overview
Prevalence and Incidence01:08

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health condition at a...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

You might also read

Related Articles

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

Sort by
Same author

A mixed-effects Bayesian regression model for multivariate group testing data.

Biometrics·2025
Same author

Bayesian Additive Regression Trees for Group Testing Data.

Statistics in medicine·2025
Same author

Regression analysis of group-tested current status data.

Biometrika·2024
Same author

binGroup2: Statistical Tools for Infection Identification via Group Testing.

The R journal·2024
Same author

Estimating the prevalence of two or more diseases using outcomes from multiplex group testing.

Biometrical journal. Biometrische Zeitschrift·2023
Same author

Discussion on "Is group testing ready for prime-time in disease identification".

Statistics in medicine·2021
Same journal

Coefficients of Determination for Mixed-Effects Models.

Journal of agricultural, biological, and environmental statistics·2026
Same journal

Identifying Relevant Covariates in RNA-seq Analysis by Pseudo-Variable Augmentation.

Journal of agricultural, biological, and environmental statistics·2026
Same journal

Assessing Simultaneous Infection with Multiple Pathogens via Group Testing with Imperfect Multiplex Assays.

Journal of agricultural, biological, and environmental statistics·2026
Same journal

MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models.

Journal of agricultural, biological, and environmental statistics·2026
Same journal

Improving Crop Model Inference Through Bayesian Melding With Spatially Varying Parameters.

Journal of agricultural, biological, and environmental statistics·2025
Same journal

Modeling Complex Spatial Dependencies: Low-Rank Spatially Varying Cross-Covariances With Application to Soil Nutrient Data.

Journal of agricultural, biological, and environmental statistics·2025
See all related articles

Related Experiment Video

Updated: May 31, 2026

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

Estimating Disease Prevalence Using Inverse Binomial Pooled Testing.

Nicholas A Pritchard1, Joshua M Tebbs

  • 1Department of Mathematics and Statistics, Coastal Carolina University, Conway, SC 29528, USA.

Journal of Agricultural, Biological, and Environmental Statistics
|July 12, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces inverse binomial sampling for pooled testing to estimate pathogen prevalence. This method is useful for early reporting in public health and agricultural risk assessments when infection rates are low.

Related Experiment Videos

Last Updated: May 31, 2026

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

Area of Science:

  • Biostatistics
  • Epidemiology
  • Public Health

Background:

  • Monitoring host and insect vector populations is crucial for agricultural and public health risk assessment.
  • Pooled (group) testing is often used to detect low-prevalence pathogens by testing groups of subjects instead of individuals.

Purpose of the Study:

  • To develop and evaluate methods for estimating population prevalence (p) using pooled testing with inverse binomial sampling.
  • To address limitations of traditional binomial models in pooled testing scenarios.

Main Methods:

  • The study applies inverse binomial sampling, a method suitable for early reporting and low disease incidence.
  • Development of point and interval estimation procedures for prevalence (p) within the inverse binomial sampling framework for pooled testing.
  • Illustration of the proposed methods using existing datasets from scientific literature.

Main Results:

  • The research provides novel statistical procedures for prevalence estimation in pooled testing settings.
  • The methods are demonstrated to be effective using real-world data, offering practical applications.

Conclusions:

  • Inverse binomial sampling offers a viable alternative to traditional binomial models for pooled testing, especially when early estimates are needed.
  • The developed estimation procedures enhance the ability to accurately assess pathogen prevalence in various risk assessment contexts.