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

Group Design02:01

Group Design

11.0K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
11.0K
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
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...
7.3K
Multiple Regression01:25

Multiple Regression

4.3K
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...
4.3K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

7.1K
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...
7.1K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

711
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...
711
Behrens–Fisher Test00:57

Behrens–Fisher Test

330
The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test...
330

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

Oxymatrine alleviates cerebral ischemia-reperfusion injury by inhibiting microglia ferroptosis via NRF2 pathway activation.

Phytomedicine : international journal of phytotherapy and phytopharmacology·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

4d orbital ruthenium doping enables high-capacity and stable α-MnO<sub>2</sub> cathodes for aqueous zinc-ion batteries.

Dalton transactions (Cambridge, England : 2003)·2026
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: Apr 7, 2026

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

6.5K

A general regression framework for group testing data, which incorporates pool dilution effects.

Dewei Wang1, Christopher S McMahan2, Colin M Gallagher2

  • 1Department of Statistics, University of South Carolina, Columbia, SC 29028, U.S.A.

Statistics in Medicine
|July 16, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible binary regression framework for analyzing group testing data, improving infectious disease screening and estimation accuracy. The method enhances diagnostic accuracy by considering individual biological markers and testing errors.

Keywords:
biomarkermeasurement errorpool testingsensitivityspecificity

More Related Videos

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.7K
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

11.0K

Related Experiment Videos

Last Updated: Apr 7, 2026

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

6.5K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.7K
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

11.0K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Infectious Disease Screening

Background:

  • Group testing, using sample pooling, is efficient for infectious disease screening, aiming for both case identification and accurate estimation.
  • Existing group testing strategies often require extensive retesting and may not fully account for diagnostic errors or individual variability.

Purpose of the Study:

  • To develop a generalizable binary regression framework for analyzing data from diverse group testing strategies.
  • To enhance the accuracy and precision of case identification and estimation in group testing studies.
  • To relax assumptions about testing error rates by incorporating individual-level biological marker information.

Main Methods:

  • Proposed a novel binary regression framework adaptable to any group testing strategy.
  • Integrated diagnostic testing results with latent biological marker levels to model testing error rates.
  • Validated the methodology through simulations and application to real-world hepatitis B data.

Main Results:

  • The proposed framework accurately and precisely analyzes group testing data from various strategies.
  • Relaxing assumptions on testing error rates improved the reliability of results.
  • Demonstrated the framework's utility with hepatitis B screening data.

Conclusions:

  • The binary regression framework offers a versatile and robust approach for analyzing group testing data in public health surveillance.
  • This methodology provides improved tools for infectious disease screening, case identification, and estimation, particularly when accounting for testing imperfections.