Journal
PREVENTIVE VETERINARY MEDICINE
Volume 113, Issue 1, Pages 13-22Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.prevetmed.2013.09.015
Keywords
Imperfect diagnostic test; Binomial confidence interval; Coverage probability; False positives; False negatives; Exact and approximate inference
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Funding
- [TAMOP-4.2.2.B-10/1]
- [TAMOP-4.2.1.B-11/2/KMR-2011-0003]
- [OTKA K108571]
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Prevalence of a disease is usually assessed by diagnostic tests that may produce false results. Rogan and Gladen (1978) described a method to estimate the true prevalence correcting for sensitivity and specificity of the diagnostic procedure, and Reiczigel et al. (2010) provided exact confidence intervals for the true prevalence assuming sensitivity and specificity were known. In this paper we propose a new method to construct approximate confidence intervals for the true prevalence when sensitivity and specificity are estimated from independent samples. To improve coverage we applied an adjustment similar to that described in Agresti and Coull (1998). According to an extensive simulation study the new confidence intervals maintain the nominal level fairly well even for sample sizes as small as 30; minimum coverage is above 88%, 93%, and 98% at nominal 90%, 95%, and 99%, respectively. We illustrate the advantages of the proposed method with real-life applications. (C) 2013 Elsevier B.V. All rights reserved.
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