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Your search keyword '"Epidemiologic Methods"' showing total 2 results
2 results on '"Epidemiologic Methods"'

1. Bayesian Estimation of Pneumonia Etiology: Epidemiologic Considerations and Applications to the Pneumonia Etiology Research for Child Health Study.

Author

Knoll, Maria Deloria, Wei Fu, Qiyuan Shi, Prosperi, Christine, Zhenke Wu, Hammitt, Laura L., Feikin, Daniel R., Baggett, Henry C., Howie, Stephen R. C., G. Scott, J. Anthony, Murdoch, David R., Madhi, Shabir A., Thea, Donald M., Brooks, W. Abdullah, Kotloff, Karen L., Mengying Li, Park, Daniel E., Wenyi Lin, Levine, Orin S., and O’Brien, Katherine L.

Subjects
*PNEUMONIA in children, *ETIOLOGY of pneumonia, *LOGISTIC regression analysis, *BAYES' estimation, *DISEASE prevalence, *CHILDREN'S health
Abstract

In pneumonia, specimens are rarely obtained directly from the infection site, the lung, so the pathogen causing infection is determined indirectly from multiple tests on peripheral clinical specimens, which may have imperfect and uncertain sensitivity and specificity, so inference about the cause is complex. Analytic approaches have included expert review of case-only results, case–control logistic regression, latent class analysis, and attributable fraction, but each has serious limitations and none naturally integrate multiple test results. The Pneumonia Etiology Research for Child Health (PERCH) study required an analytic solution appropriate for a case–control design that could incorporate evidence from multiple specimens from cases and controls and that accounted for measurement error. We describe a Bayesian integrated approach we developed that combined and extended elements of attributable fraction and latent class analyses to meet some of these challenges and illustrate the advantage it confers regarding the challenges identified for other methods. [ABSTRACT FROM AUTHOR]

Published
2017
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2. Bayesian estimation of pneumonia etiology: Epidemiologic considerations and applications to the Pneumonia Etiology Research for Child Health study

Author

Maria Deloria Knoll, Christine Prosperi, Orin S. Levine, Qiyuan Shi, Shabir A. Madhi, Laura L. Hammitt, Zhenke Wu, Henry C. Baggett, Stephen R. C. Howie, Katherine L. O'Brien, Donald M. Thea, Daniel R. Feikin, Karen L. Kotloff, W. Abdullah Brooks, Mengying Li, Wei Fu, J. Anthony G. Scott, Scott L. Zeger, Daniel E. Park, Wenyi Lin, and David R. Murdoch

Subjects
0301 basic medicine, Microbiology (medical), medicine.medical_specialty, Pathology, Bayes theorem, Biomedical Research, 030106 microbiology, Bayesian probability, epidemiologic methods, Pneumonia, Viral, Inference, Diagnostic Techniques, Respiratory System, statistical models, Logistic regression, Sensitivity and Specificity, 03 medical and health sciences, Bayes' theorem, 0302 clinical medicine, medicine, Pneumonia, Bacterial, Humans, 030212 general & internal medicine, Intensive care medicine, Child, etiologic estimations, Models, Statistical, business.industry, Child Health, Pneumonia, medicine.disease, Latent class model, 3. Good health, Infectious Diseases, Case-Control Studies, Epidemiologic Research Design, Attributable risk, Etiology, Supplement Article, business
Abstract

In pneumonia, specimens are rarely obtained directly from the infection site, the lung, so the pathogen causing infection is determined indirectly from multiple tests on peripheral clinical specimens, which may have imperfect and uncertain sensitivity and specificity, so inference about the cause is complex. Analytic approaches have included expert review of case-only results, case–control logistic regression, latent class analysis, and attributable fraction, but each has serious limitations and none naturally integrate multiple test results. The Pneumonia Etiology Research for Child Health (PERCH) study required an analytic solution appropriate for a case–control design that could incorporate evidence from multiple specimens from cases and controls and that accounted for measurement error. We describe a Bayesian integrated approach we developed that combined and extended elements of attributable fraction and latent class analyses to meet some of these challenges and illustrate the advantage it confers regarding the challenges identified for other methods.

Published
2017

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