Informative Group Testing for Multiplex Assays

Infectious disease testing frequently takes advantage of two tools–group testing and multiplex assays–to make testing timely and cost effective. Until the work of Tebbs et al. (2013) and Hou et al. (2017), there was no research available to understand how best to apply these tools simultaneously. This recent work focused on applications where each individual is considered to be identical in terms of the probability of disease. However, risk-factor information, such as past behavior and presence of symptoms, is very often available on each individual to allow one to estimate individual-specific probabilities. The purpose of our paper is to propose the first group testing algorithms for multiplex assays that take advantage of individual risk-factor information as expressed by these probabilities. We show that our methods significantly reduce the number of tests required while preserving accuracy. Throughout this paper, we focus on applying our methods with the Aptima Combo 2 Assay that is used worldwide for chlamydia and gonorrhea screening

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

When screening a population for infectious diseases, pooling individual specimens (e.g., blood, swabs, urine, etc.) can provide enormous cost savings when compared to testing specimens individually. In the biostatistics literature, testing pools of specimens is commonly known as group testing or pooled testing. Although estimating a population-level prevalence with group testing data has received a large amount of attention, most of this work has focused on applications involving a single disease, such as human immunodeficiency virus. Modern methods of screening now involve testing pools and individuals for multiple diseases simultaneously through the use of multiplex assays. Hou et al. (2017, Biometrics, 73, 656–665) and Hou et al. (2020, Biostatistics, 21, 417–431) recently proposed group testing protocols for multiplex assays and derived relevant case identification characteristics, including the expected number of tests and those which quantify classification accuracy. In this article, we describe Bayesian methods to estimate population-level disease probabilities from implementing these protocols or any other multiplex group testing protocol which might be carried out in practice. Our estimation methods can be used with multiplex assays for two or more diseases while incorporating the possibility of test misclassification for each disease. We use chlamydia and gonorrhea testing data collected at the State Hygienic Laboratory at the University of Iowa to illustrate our work. We also provide an online R resource practitioners can use to implement the methods in this article

binGroup2: Statistical Tools for Infection Identification via Group Testing

Group testing is the process of testing items as an amalgamation, rather than separately, to determine the binary status for each item. Its use was especially important during the COVID-19 pandemic through testing specimens for SARS-CoV-2. The adoption of group testing for this and many other applications is because members of a negative testing group can be declared negative with potentially only one test. This subsequently leads to significant increases in laboratory testing capacity. Whenever a group testing algorithm is put into practice, it is critical for laboratories to understand the algorithm’s operating characteristics, such as the expected number of tests. Our paper presents the binGroup2 package that provides the statistical tools for this purpose. This R package is the first to address the identification aspect of group testing for a wide variety of algorithms. We illustrate its use through COVID-19 and chlamydia/gonorrhea applications of group testing

Author Correction:Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article

Star Library: What is the Shelf Life?

The Food and Drug Administration requires pharmaceutical companies to establish a shelf life for all new drug products through a stability analysis. This is done to ensure the quality of the drug taken by an individual is within established levels. The purpose of this out-of-class project or in-class example is to determine the shelf life of a new drug. This is done through using simple linear regression models and correctly interpreting confidence and prediction intervals. An Excel spreadsheet and SAS program are given to help perform the analysis

Discussion on “Is group testing ready for prime-time in disease identification”

Is group testing ready for prime-time in disease identification? Yes! Prior to the COVID-19 pandemic, group testing (also known as pooled testing and specimen pooling) was widely used in areas including blood donation screening,1,2 infectious disease testing in animals,3,4 sexually transmitted infection testing,5,6 and surveillance for pathogen contamination of food.7,8 More areas could definitely benefit as well, as pointed out by Haber et al9 (HMA). The use of group testing for SARS-CoV-2 detection was isolated at the beginning of the pandemic, but early accounts of its successful implementation10-12 with little if any loss of accuracy led to its widespread use. More than 100 papers detail its implementation since April 2020. A large number of news media accounts, such as in the Washington Post,13 ABC News,14 and National Public Radio,15 likely led to its adoption as well. Large laboratories, like LabCorp16 and Quest Diagnostics,17 received Emergency Use Authorizations (EUAs) from the Food and Drug Administration to use their assays with group testing. There is even a Wikipedia18 web page dedicated to the use of group testing during the pandemic. Therefore, not only is group testing ready for “prime-time”, but it is an established “hit” among laboratories around the world to increase testing capacity. The focus of HMA is on the accuracy of group testing, in particular the first-stage sensitivity for Dorfman testing. We completely agree that quantifying this accuracy is extremely important for laboratories. Where we differ from HMA is how to account for the first-stage sensitivity. In our discussion, we provide comments on the methods used by HMA. Next, we discuss how laboratories choose a group size to insure first-stage sensitivity is not compromised. Relative to this discussion, we provide information about how to find the optimal group size. We conclude with reasons beyond accuracy for why some laboratories may remain hesitant toward group testing. We also conclude with what is next for laboratory implementation of group testing. Because of the worldwide importance of SARS-CoV-2 detection right now, our discussion focuses mostly on this group testing application

Analysis of categories data with R

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Star Library: Which Paper Towel is More Absorbent?

This group activity focuses on conducting an experiment to determine which of two brands of paper towels are more absorbent by measuring the amount of water absorbed. A two-sample t-test can be used to analyze the data, or simple graphics and descriptive statistics can be used as an exploratory analysis. Students are asked to think about design issues, and to write a short report stating their results and conclusions, along with an evaluation of the experimental design