Statistics Papers

Document Type

Journal Article

Date of this Version

8-2012

Publication Source

Epidemiologic Methods

Volume

1

Issue

1

Start Page

33

Last Page

54

DOI

10.1515/2161-962X.1005

Abstract

Statistical methods such as latent class analysis can estimate the sensitivity and specificity of diagnostic tests when no perfect reference test exists. Traditional latent class methods assume a constant disease prevalence in one or more tested populations. When the risk of disease varies in a known way, these models fail to take advantage of additional information that can be obtained by measuring risk factors at the level of the individual. We show that by incorporating complex field-based epidemiologic data, in which the disease prevalence varies as a continuous function of individual-level covariates, our model produces more accurate sensitivity and specificity estimates than previous methods. We apply this technique to several simulated populations and to actual Chagas disease test data from a community near Arequipa, Peru. Results from our model estimate that the first-line enzyme-linked immunosorbent assay has a sensitivity of 78% (95% CI: 62-100%) and a specificity of 100% (95% CI: 99-100%). The confirmatory immunofluorescence assay is estimated to be 73% sensitive (95% CI: 65-81%) and 99% specific (95% CI: 96-100%).

Copyright/Permission Statement

The final publication is available at www.degruyter.com

Keywords

chagas disease, latent class analysis, Trypanosoma cruzi

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Date Posted: 27 November 2017

This document has been peer reviewed.