1. Latent class analysis: insights about design and analysis of schistosomiasis diagnostic studies
- Author
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Irini Moustaki, Haziq Jamil, Artemis Koukounari, Clive Shiff, Elena A. Erosheva, Imperial College London, Universiti Brunei Darussalam, Modèles et algorithmes pour l’intelligence artificielle (MAASAI), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Laboratoire Jean Alexandre Dieudonné (LJAD), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Johns Hopkins Bloomberg School of Public Health [Baltimore], Johns Hopkins University (JHU), and London School of Economics and Political Science (LSE)
- Subjects
0301 basic medicine ,Schistosoma Mansoni ,Computer science ,Physiology ,Eggs ,RC955-962 ,Test Statistics ,Urine ,0302 clinical medicine ,Medical Conditions ,Mathematical and Statistical Techniques ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,[SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases ,Reproductive Physiology ,Arctic medicine. Tropical medicine ,Medicine and Health Sciences ,Schistosomiasis ,Simulation and Modeling ,Statistics ,Eukaryota ,Reference Standards ,[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,Latent class model ,Body Fluids ,Infectious Diseases ,Risk analysis (engineering) ,Conditional independence ,Latent Class Analysis ,Helminth Infections ,Physical Sciences ,Schistosoma ,Public aspects of medicine ,RA1-1270 ,Anatomy ,Research Article ,Neglected Tropical Diseases ,030231 tropical medicine ,Context (language use) ,Research and Analysis Methods ,Sensitivity and Specificity ,03 medical and health sciences ,Diagnostic Medicine ,Helminths ,medicine ,Parasitic Diseases ,Humans ,Animals ,Diagnostic Errors ,Statistical Methods ,Statistical hypothesis testing ,Models, Statistical ,Diagnostic Tests, Routine ,Public Health, Environmental and Occupational Health ,Organisms ,Biology and Life Sciences ,Gold standard (test) ,medicine.disease ,Tropical Diseases ,Invertebrates ,Schistosoma Haematobium ,030104 developmental biology ,Global Health Initiatives ,RA Public aspects of medicine ,Biostatistics ,Zoology ,Mathematics - Abstract
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., Author summary Accurate schistosomiasis diagnosis is essential to assess the impact of large scale and repeated mass drug administration to control or even eliminate this disease. However, in schistosomiasis diagnostic studies, several inherent study design issues pose a real challenge for the currently available statistical tools used for diagnostic modelling and associated data analysis and conclusions. More specifically, those study design issues are: 1) the inclusion of small number of diagnostic tests (i.e. most often five), 2) non formal consensus about a schistosomiasis gold standard, 3) the contemporary use of relatively small sample sizes in relevant studies due to lack of research funding, 4) the differing levels of prevalence of the studied disease even within the same area of one endemic country and 5) other real world factors such as: the lack of appropriate equipment, the variability of certain methods due to biological phenomena and training of technicians across the endemic countries because of scarce financial resources contributing to the existing lack of a schistosomiasis gold standard. The current study aims to caution practitioners from blindly applying statistical models with small number of diagnostic tests and sample sizes, proposing design guidelines of future schistosomiasis diagnostic accuracy studies with recommendations for further research. While our study is centred around the diagnosis of schistosomiasis, we feel that the recommendations can be adapted to other major tropical infectious diseases as well.
- Published
- 2021
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