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 Standard Deviation01:19

Testing a Claim about Standard Deviation

2.7K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.7K
Regression Analysis01:11

Regression Analysis

6.9K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.9K
Multiple Regression01:25

Multiple Regression

3.4K
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...
3.4K
Solution Concentration and Dilution02:59

Solution Concentration and Dilution

97.5K
The relative amount of a given solution component is known as its concentration. Often, though not always, a solution contains one component with a concentration that is significantly greater than that of all other components. This component is called the solvent and may be viewed as the medium in which the other components are dispersed or dissolved. Solutions in which water is the solvent are, of course, very common on our planet. A solution in which water is the solvent is called an aqueous...
97.5K
Regression Toward the Mean01:52

Regression Toward the Mean

6.6K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.6K
Bonferroni Test01:10

Bonferroni Test

3.1K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
3.1K

You might also read

Related Articles

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

Sort by
Same author

Temporal analysis of physiological phenotypes identifies metabolic and genetic underpinnings of senescence in maize.

The Plant cell·2025
Same author

Machine Learning and Probabilistic Approaches for Forecasting COVID-19 Transmission and Cases.

medRxiv : the preprint server for health sciences·2025
Same author

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

Biometrics·2025
Same author

Heart rate variability derangements in dogs with Chagas disease: a potential indicator of autonomic and cardiac disruption.

Journal of the American Veterinary Medical Association·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 journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Nov 17, 2025

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

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

Published on: May 13, 2022

6.2K

Incorporating the dilution effect in group testing regression.

Stefani C Mokalled1, Christopher S McMahan1, Joshua M Tebbs2

  • 1School of Mathematical and Statistical Sciences, Clemson University, Clemson, South Carolina, USA.

Statistics in Medicine
|February 18, 2021
PubMed
Summary
This summary is machine-generated.

Group testing offers cost savings for infectious disease screening but faces a dilution effect. This study introduces a new statistical framework for pooled samples, improving disease probability estimation and case identification accuracy.

Keywords:
biomarkerexpectation-maximization algorithmlatent datamixture modelpooled testingspecimen pooling

More Related Videos

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

15.7K
Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

19.1K

Related Experiment Videos

Last Updated: Nov 17, 2025

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

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

Published on: May 13, 2022

6.2K
Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

15.7K
Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

19.1K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Infectious Disease Screening

Background:

  • Group testing is a cost-effective method for infectious disease screening, testing pooled specimens instead of individual ones.
  • A key challenge in group testing is the "dilution effect," where positive signals are obscured in large negative pools.

Purpose of the Study:

  • To propose a novel statistical framework for group testing data that integrates estimation and case identification.
  • To address the dilution effect by analyzing continuous biomarker levels in pooled samples.

Main Methods:

  • Developed a framework analyzing continuous biomarker levels from pooled samples.
  • Estimated a binary regression model for disease probability and biomarker distributions for cases and controls.
  • Utilized biomarker distribution estimates to optimize pool-specific diagnostic thresholds for enhanced case identification.

Main Results:

  • The proposed framework effectively merges estimation and case identification, improving accuracy.
  • Pool-by-pool threshold selection based on biomarker distributions enhances diagnostic precision.
  • Demonstrated utility using hepatitis B virus data from an Irish prison population.

Conclusions:

  • The new statistical approach offers a more accurate and efficient method for infectious disease screening using group testing.
  • This framework mitigates the dilution effect, leading to improved identification of infectious individuals.