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Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study
BACKGROUND: Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical an...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4307378/ https://www.ncbi.nlm.nih.gov/pubmed/25592782 http://dx.doi.org/10.1186/s12859-014-0402-2 |
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author | Walker, Martin Basáñez, María-Gloria Ouédraogo, André Lin Hermsen, Cornelus Bousema, Teun Churcher, Thomas S |
author_facet | Walker, Martin Basáñez, María-Gloria Ouédraogo, André Lin Hermsen, Cornelus Bousema, Teun Churcher, Thomas S |
author_sort | Walker, Martin |
collection | PubMed |
description | BACKGROUND: Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical and epidemiological contexts. These methods are often classified as semi-quantitative, yet estimates of reliability or sensitivity are seldom reported. Here, a statistical framework is developed for assessing the reliability (uncertainty) of pathogen densities estimated using QMMs and the associated diagnostic sensitivity. The method is illustrated with quantification of Plasmodium falciparum gametocytaemia by QT-NASBA. RESULTS: The reliability of pathogen (e.g. gametocyte) densities, and the accompanying diagnostic sensitivity, estimated by two contrasting statistical calibration techniques, are compared; a traditional method and a mixed model Bayesian approach. The latter accounts for statistical dependence of QMM assays run under identical laboratory protocols and permits structural modelling of experimental measurements, allowing precision to vary with pathogen density. Traditional calibration cannot account for inter-assay variability arising from imperfect QMMs and generates estimates of pathogen density that have poor reliability, are variable among assays and inaccurately reflect diagnostic sensitivity. The Bayesian mixed model approach assimilates information from replica QMM assays, improving reliability and inter-assay homogeneity, providing an accurate appraisal of quantitative and diagnostic performance. CONCLUSIONS: Bayesian mixed model statistical calibration supersedes traditional techniques in the context of QMM-derived estimates of pathogen density, offering the potential to improve substantially the depth and quality of clinical and epidemiological inference for a wide variety of pathogens. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0402-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4307378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43073782015-02-03 Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study Walker, Martin Basáñez, María-Gloria Ouédraogo, André Lin Hermsen, Cornelus Bousema, Teun Churcher, Thomas S BMC Bioinformatics Methodology Article BACKGROUND: Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical and epidemiological contexts. These methods are often classified as semi-quantitative, yet estimates of reliability or sensitivity are seldom reported. Here, a statistical framework is developed for assessing the reliability (uncertainty) of pathogen densities estimated using QMMs and the associated diagnostic sensitivity. The method is illustrated with quantification of Plasmodium falciparum gametocytaemia by QT-NASBA. RESULTS: The reliability of pathogen (e.g. gametocyte) densities, and the accompanying diagnostic sensitivity, estimated by two contrasting statistical calibration techniques, are compared; a traditional method and a mixed model Bayesian approach. The latter accounts for statistical dependence of QMM assays run under identical laboratory protocols and permits structural modelling of experimental measurements, allowing precision to vary with pathogen density. Traditional calibration cannot account for inter-assay variability arising from imperfect QMMs and generates estimates of pathogen density that have poor reliability, are variable among assays and inaccurately reflect diagnostic sensitivity. The Bayesian mixed model approach assimilates information from replica QMM assays, improving reliability and inter-assay homogeneity, providing an accurate appraisal of quantitative and diagnostic performance. CONCLUSIONS: Bayesian mixed model statistical calibration supersedes traditional techniques in the context of QMM-derived estimates of pathogen density, offering the potential to improve substantially the depth and quality of clinical and epidemiological inference for a wide variety of pathogens. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0402-2) contains supplementary material, which is available to authorized users. BioMed Central 2015-01-16 /pmc/articles/PMC4307378/ /pubmed/25592782 http://dx.doi.org/10.1186/s12859-014-0402-2 Text en © Walker et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Walker, Martin Basáñez, María-Gloria Ouédraogo, André Lin Hermsen, Cornelus Bousema, Teun Churcher, Thomas S Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study |
title | Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study |
title_full | Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study |
title_fullStr | Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study |
title_full_unstemmed | Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study |
title_short | Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study |
title_sort | improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4307378/ https://www.ncbi.nlm.nih.gov/pubmed/25592782 http://dx.doi.org/10.1186/s12859-014-0402-2 |
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