Latent Class Analysis: Insights about design and analysis of schistosomiasis diagnostic studies.

Various global health initiatives are currently advocating the elimination of schistosomiasis within the next decade. Schistosomiasis is a highly debilitating tropical infectious disease with severe burden of morbidity and thus operational research accurately evaluating diagnostics that quantify the...

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Main Authors: Artemis Koukounari, Haziq Jamil, Elena Erosheva, Clive Shiff, Irini Moustaki
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-02-01
Series:PLoS Neglected Tropical Diseases
Online Access:https://doi.org/10.1371/journal.pntd.0009042
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author Artemis Koukounari
Haziq Jamil
Elena Erosheva
Clive Shiff
Irini Moustaki
author_facet Artemis Koukounari
Haziq Jamil
Elena Erosheva
Clive Shiff
Irini Moustaki
author_sort Artemis Koukounari
collection DOAJ
description Various global health initiatives are currently advocating the elimination of schistosomiasis within the next decade. Schistosomiasis is a highly debilitating tropical infectious disease with severe burden of morbidity and thus operational research accurately evaluating diagnostics that quantify the epidemic status for guiding effective strategies is essential. Latent class models (LCMs) have been generally considered in epidemiology and in particular in recent schistosomiasis diagnostic studies as a flexible tool for evaluating diagnostics because assessing the true infection status (via a gold standard) is not possible. However, within the biostatistics literature, classical LCM have already been criticised for real-life problems under violation of the conditional independence (CI) assumption and when applied to a small number of diagnostics (i.e. most often 3-5 diagnostic tests). Solutions of relaxing the CI assumption and accounting for zero-inflation, as well as collecting partial gold standard information, have been proposed, offering the potential for more robust model estimates. In the current article, we examined such approaches in the context of schistosomiasis via analysis of two real datasets and extensive simulation studies. Our main conclusions highlighted poor model fit in low prevalence settings and the necessity of collecting partial gold standard information in such settings in order to improve the accuracy and reduce bias of sensitivity and specificity estimates.
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spelling doaj.art-75773697fdd546c4b2d8a5339c905d472022-12-21T19:16:35ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352021-02-01152e000904210.1371/journal.pntd.0009042Latent Class Analysis: Insights about design and analysis of schistosomiasis diagnostic studies.Artemis KoukounariHaziq JamilElena EroshevaClive ShiffIrini MoustakiVarious global health initiatives are currently advocating the elimination of schistosomiasis within the next decade. Schistosomiasis is a highly debilitating tropical infectious disease with severe burden of morbidity and thus operational research accurately evaluating diagnostics that quantify the epidemic status for guiding effective strategies is essential. Latent class models (LCMs) have been generally considered in epidemiology and in particular in recent schistosomiasis diagnostic studies as a flexible tool for evaluating diagnostics because assessing the true infection status (via a gold standard) is not possible. However, within the biostatistics literature, classical LCM have already been criticised for real-life problems under violation of the conditional independence (CI) assumption and when applied to a small number of diagnostics (i.e. most often 3-5 diagnostic tests). Solutions of relaxing the CI assumption and accounting for zero-inflation, as well as collecting partial gold standard information, have been proposed, offering the potential for more robust model estimates. In the current article, we examined such approaches in the context of schistosomiasis via analysis of two real datasets and extensive simulation studies. Our main conclusions highlighted poor model fit in low prevalence settings and the necessity of collecting partial gold standard information in such settings in order to improve the accuracy and reduce bias of sensitivity and specificity estimates.https://doi.org/10.1371/journal.pntd.0009042
spellingShingle Artemis Koukounari
Haziq Jamil
Elena Erosheva
Clive Shiff
Irini Moustaki
Latent Class Analysis: Insights about design and analysis of schistosomiasis diagnostic studies.
PLoS Neglected Tropical Diseases
title Latent Class Analysis: Insights about design and analysis of schistosomiasis diagnostic studies.
title_full Latent Class Analysis: Insights about design and analysis of schistosomiasis diagnostic studies.
title_fullStr Latent Class Analysis: Insights about design and analysis of schistosomiasis diagnostic studies.
title_full_unstemmed Latent Class Analysis: Insights about design and analysis of schistosomiasis diagnostic studies.
title_short Latent Class Analysis: Insights about design and analysis of schistosomiasis diagnostic studies.
title_sort latent class analysis insights about design and analysis of schistosomiasis diagnostic studies
url https://doi.org/10.1371/journal.pntd.0009042
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