Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31053
Title: Prevalence estimation subject to misclassification : the mis-substitution bias and some remedies
Authors: Zhang, Z
Liu, C 
Kim, S
Liu, A
Keywords: Dilution effect
Group testing
Maximum likelihood
Optimal design
Pooled testing
Sensitivity
Specificity
Test error
Issue Date: 2014
Publisher: John Wiley and Sons Ltd
Source: Statistics in medicine, 2014, v. 33, no. 25, p. 4482-4500 How to cite?
Journal: Statistics in Medicine 
Abstract: We consider the problem of estimating the prevalence of a disease under a group testing framework. Because assays are usually imperfect, misclassification of disease status is a major challenge in prevalence estimation. To account for possible misclassification, it is usually assumed that the sensitivity and specificity of the assay are known and independent of the group size. This assumption is often questionable, and substitution of incorrect values of an assay's sensitivity and specificity can result in a large bias in the prevalence estimate, which we refer to as the mis-substitution bias. In this article, we propose simple designs and methods for prevalence estimation that do not require known values of assay sensitivity and specificity. If a gold standard test is available, it can be applied to a validation subsample to yield information on the imperfect assay's sensitivity and specificity. When a gold standard is unavailable, it is possible to estimate assay sensitivity and specificity, either as unknown constants or as specified functions of the group size, from group testing data with varying group size. We develop methods for estimating parameters and for finding or approximating optimal designs, and perform extensive simulation experiments to evaluate and compare the different designs. An example concerning human immunodeficiency virus infection is used to illustrate the validation subsample design.
URI: http://hdl.handle.net/10397/31053
ISSN: 0277-6715
DOI: 10.1002/sim.6268
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