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Nonparametric estimation of distributions and diagnostic accuracy based on group‐tested results with differential misclassification
Author(s) -
Zhang Wei,
Liu Aiyi,
Li Qizhai,
Albert Paul S.
Publication year - 2020
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13236
Subject(s) - nonparametric statistics , statistics , estimator , group testing , population , estimation , differential (mechanical device) , distribution (mathematics) , mathematics , econometrics , computer science , medicine , environmental health , management , engineering , economics , aerospace engineering , mathematical analysis , combinatorics
This article concerns the problem of estimating a continuous distribution in a diseased or nondiseased population when only group‐based test results on the disease status are available. 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‐reactive protein in blood samples in predicting chlamydia incidence.