{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T04:15:24Z","timestamp":1789445724060,"version":"build-2803163510"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2018,9,29]],"date-time":"2018-09-29T00:00:00Z","timestamp":1538179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"},{"start":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T00:00:00Z","timestamp":1552003200000},"content-version":"vor","delay-in-days":160,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["AI121351"],"award-info":[{"award-number":["AI121351"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["AI121351"],"award-info":[{"award-number":["AI121351"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>For disease screening, group (pooled) testing can be a cost-saving alternative to one-at-a-time testing, with savings realized through assaying pooled biospecimen (eg, urine, blood, saliva). In many group testing settings, practitioners are faced with the task of conducting disease surveillance. That is, it is often of interest to relate individuals\u2019 true disease statuses to covariate information via binary regression. Several authors have developed regression methods for group testing data, which is challenging due to the effects of imperfect testing. That is, all testing outcomes (on pools and individuals) are subject to misclassification, and individuals\u2019 true statuses are never observed. To further complicate matters, individuals may be involved in several testing outcomes. For analyzing such data, we provide a novel regression methodology which generalizes and extends the aforementioned regression techniques and which incorporates regularization. Specifically, for model fitting and variable selection, we propose an adaptive elastic net estimator under the logistic regression model which can be used to analyze data from any group testing strategy. We provide an efficient algorithm for computing the estimator along with guidance on tuning parameter selection. Moreover, we establish the asymptotic properties of the proposed estimator and show that it possesses \u201coracle\u201d properties. We evaluate the performance of the estimator through Monte Carlo studies and illustrate the methodology on a chlamydia data set from the State Hygienic Laboratory in Iowa City.<\/jats:p>","DOI":"10.1111\/biom.12973","type":"journal-article","created":{"date-parts":[[2018,9,29]],"date-time":"2018-09-29T09:58:14Z","timestamp":1538215094000},"page":"13-23","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Adaptive Elastic Net for Group Testing"],"prefix":"10.1093","volume":"75","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7611-0906","authenticated-orcid":false,"given":"Karl B.","family":"Gregory","sequence":"first","affiliation":[{"name":"Department of Statistics, University of South Carolina , Columbia, South Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0822-8563","authenticated-orcid":false,"given":"Dewei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of South Carolina , Columbia, South Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5056-9615","authenticated-orcid":false,"given":"Christopher S.","family":"McMahan","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Clemson University , Clemson, South Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,9,29]]},"reference":[{"key":"2024011509394476700_biom12973-bib-0001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/biomet\/71.1.1","article-title":"On the existence of maximum likelihood estimates in logistic regression models","volume":"71","author":"Albert","year":"1984","journal-title":"Biometrika"},{"key":"2024011509394476700_biom12973-bib-0002","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-20192-9","volume-title":"Statistics for High-Dimensional Data. 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