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The problem is challenging in that individual disease statuses are not observed and testing results are often subject to misclassification, with further complication that the misclassification may be differential as the group size and the number of the diseased individuals in the group vary. We propose a method to construct nonparametric estimation of the distribution and obtain its asymptotic properties. The performance of the distribution estimator is evaluated under various design considerations concerning group sizes and classification errors. The method is exemplified with data from the National Health and Nutrition Examination Survey study to estimate the distribution and diagnostic accuracy of C\u2010reactive protein in blood samples in predicting chlamydia\u00a0incidence.<\/jats:p>","DOI":"10.1111\/biom.13236","type":"journal-article","created":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T15:29:55Z","timestamp":1582298995000},"page":"1147-1156","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Nonparametric estimation of distributions and diagnostic accuracy based on group\u2010tested results with differential misclassification"],"prefix":"10.1093","volume":"76","author":[{"given":"Wei","family":"Zhang","sequence":"first","affiliation":[{"name":"LSC NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 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