Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Comparing the Survival Analysis of Two or More Groups

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...

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...

Statistical Hypothesis Testing

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

Test for Homogeneity

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...

Statistical Methods to Analyze Parametric Data: ANOVA

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...

Multiple Regression

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

You might also read

Related Articles

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

Sort by

Same author

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026

Same author

Factors affecting detection and quantification of Schistosoma haematobium eggs in pooled urine samples.

PLoS neglected tropical diseases·2026

Same author

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

Journal of agricultural, biological, and environmental statistics·2026

Same author

Consumption of Reinforcing Solutions Engages Dynamic Activity of the Prelimbic Cortical Outputs.

bioRxiv : the preprint server for biology·2026

Same author

Granular insights: A wastewater-based machine learning approach for localized COVID-19 hospitalization forecasting.

Epidemics·2026

Same author

High-density Phenotype Data of Intermediate Phenotypes Associated with Stalk Lodging Resistance in Maize.

Scientific data·2025

Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026

Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026

Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026

Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026

Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026

Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 28, 2025

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Generalized additive regression for group testing data.

Yan Liu¹, Christopher S McMahan², Joshua M Tebbs³

¹School of Community Health Sciences, University of Nevada, Reno, 1664 N. Virginia St, Reno, NV 89557, USA.

Biostatistics (Oxford, England)

|February 16, 2020

Summary

This summary is machine-generated.

Group testing offers cost-effective disease screening for low-prevalence conditions. A new Bayesian approach models disease probability using flexible regression, improving accuracy for group testing data with potential misclassification.

Keywords:

Bayesian regression Binary regression Gaussian predictive process Gaussian process Pooled testing Specimen pooling

More Related Videos

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

Related Experiment Videos

Last Updated: Dec 28, 2025

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

Area of Science:

Biostatistics
Epidemiology
Public Health

Background:

Group testing is a cost-effective strategy for screening low-prevalence diseases.
Traditional regression methods for group testing often assume linear covariate effects, which can lead to biased results.
Accurate estimation of disease probability requires flexible modeling of covariate relationships.

Purpose of the Study:

To propose a flexible Bayesian generalized additive regression framework for analyzing group testing data.
To accommodate potentially misclassified assay results within the group testing framework.
To estimate smooth functions of covariates, linear effects, and assay accuracy probabilities.

Main Methods:

Bayesian generalized additive regression modeling.
Incorporation of smooth functions for covariate effects.
Estimation of assay classification accuracy probabilities.
Application to group testing data with potential misclassification.

Main Results:

The proposed method provides a flexible framework for analyzing complex group testing data.
It allows for the estimation of non-linear covariate effects and assay accuracy.
Demonstrated utility using chlamydia infection data from Iowa.

Conclusions:

The Bayesian generalized additive regression approach offers a more robust and flexible alternative to linear models for group testing data.
This method enhances the accuracy of disease probability estimation in screening applications.
The framework is applicable to various group testing protocols and disease surveillance scenarios.