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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

110
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...
110
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

1.8K
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...
1.8K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

127
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
127
Multiple Regression01:25

Multiple Regression

2.9K
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...
2.9K
Two-Way ANOVA01:17

Two-Way ANOVA

2.6K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
2.6K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K

You might also read

Related Articles

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

Sort by
Same author

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

PLoS neglected tropical diseases·2026
Same author

Pooled testing for TB: revisiting a cost-saving innovation.

The Lancet. Respiratory medicine·2025
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

Performance of Urine Reagent Test Strips in Detecting <i>Schistosoma haematobium</i> Infection in Individual and Pooled Urine Samples.

Microorganisms·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 journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
Same journal

A Bayesian phase I/II platform design with data augmentation accounting for delayed outcomes.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: May 21, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

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

Christopher S McMahan1, Chase N Joyner1, Joshua M Tebbs2

  • 1School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, United States.

Biometrics
|March 21, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian framework for analyzing multiplex group testing data, improving infectious disease surveillance efficiency. The method accurately estimates disease prevalence and correlations, overcoming complex data challenges.

Keywords:
generalized linear mixed modellatent variable modelmultiplex assaymultivariate probit modelpooled testing

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.2K
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

5.8K

Related Experiment Videos

Last Updated: May 21, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.2K
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

5.8K

Area of Science:

  • Statistics
  • Epidemiology
  • Biostatistics

Background:

  • Group testing and multiplex assays enhance infectious disease screening efficiency.
  • Complex data structures from these methods can impede public health surveillance.
  • A statistical framework is needed to manage this complexity.

Purpose of the Study:

  • To develop a general Bayesian framework for analyzing multiplex group testing data.
  • To address challenges in infectious disease surveillance posed by complex data structures.
  • To enable accurate estimation of disease prevalence and correlations.

Main Methods:

  • A mixed multivariate probit model was developed for group testing data.
  • The framework incorporates correlations between disease statuses and population subgroup heterogeneity.
  • Spike and slab priors were used for automated variable selection.
  • A posterior sampling algorithm was created for model fitting.

Main Results:

  • The Bayesian framework successfully estimates disease prevalence from multiplex group testing data.
  • The model accounts for complex dependencies and population heterogeneity.
  • Numerical studies and real-world data analysis (chlamydia, gonorrhea) demonstrate methodology effectiveness.

Conclusions:

  • The proposed Bayesian framework offers a robust solution for analyzing multiplex group testing data.
  • This approach enhances the efficiency and accuracy of infectious disease surveillance.
  • The methodology is adaptable to various group testing protocols and multiplex assay designs.