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

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About: Bayesian Estimation of Pneumonia Etiology: Epidemiologic Considerations and Applications to the Pneumonia Etiology Research for Child Health Study Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Bayesian Estimation of Pneumonia Etiology: Epidemiologic Considerations and Applications to the Pneumonia Etiology Research for Child Health Study
Creator	Feikin, Daniel Murdoch, David Fu, Wei Li, Mengying Prosperi, Christine Shi, Qiyuan Scott, G Lin, Wenyi Wu, Zhenke Madhi, Shabir Anthony, J Baggett, Henry Brooks, W Howie, Stephen Knoll, Maria Kotloff, Karen Levine, Orin O'brien, Katherine Park, Daniel Thea, Donald Hammitt, Laura Knoll, M Zeger, Scott
Source	PMC
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.
has issue date	2017-06-15(xsd:dateTime)
bibo:doi	10.1093/cid/cix144
bibo:pmid	28575370
has license	cc-by
sha1sum (hex)	8276d64861e070805d56b292163da25b0240253f
schema:url	https://doi.org/10.1093/cid/cix144
resource representing a document's title	Bayesian Estimation of Pneumonia Etiology: Epidemiologic Considerations and Applications to the Pneumonia Etiology Research for Child Health Study
has PubMed Central identifier	PMC5447849
has PubMed identifier	28575370
schema:publication	Clin Infect Dis
resource representing a document's body	covid:8276d64861e070805d56b292163da25b0240253f#body_text
is schema:about of	named entity 'CASE-CONTROL DESIGN' named entity 'CASES' named entity 'PNEUMONIA' named entity 'MULTIPLE' named entity 'RESULTS' named entity 'REQUIRED' named entity 'MEET' named entity 'DETERMINED' named entity 'RESEARCH' named entity 'ABOUT' named entity 'DESCRIBE' named entity 'LUNG' named entity 'APPROPRIATE' named entity 'INCLUDED' named entity 'MAY HAVE' named entity 'EVIDENCE' named entity 'CASE' named entity 'PNEUMONIA' named entity 'CHILD HEALTH STUDY' named entity 'CONSIDERATIONS' named entity 'PATHOGEN CAUSING INFECTION' named entity 'INFERENCE' named entity 'RARELY' named entity 'TESTS' named entity 'SPECIMENS' named entity 'CLASS' named entity 'ELEMENTS' named entity 'UNCERTAIN' named entity 'CHILD HEALTH' named entity 'CHALLENGES' named entity 'DIRECTLY' named entity 'THESE' named entity 'COMPLEX' named entity 'PERCH' named entity 'INFECTIOUS DISEASES' named entity 'EPIDEMIOLOGIC' named entity 'ANALYSIS' named entity 'ETIOLOGY' named entity 'CID' named entity 'RESEARCH' named entity 'APPLICATIONS' named entity 'CLINICAL' named entity 'INTEGRATED' named entity 'EXTENDED' named entity 'CASE-CONTROL' named entity 'APPROACHES' named entity 'COMBINED' named entity 'STUDY' named entity 'APPROACH' named entity 'ATTRIBUTABLE' named entity 'SENSITIVITY AND SPECIFICITY' named entity 'REVIEW OF' named entity 'CLINICAL' named entity 'OBTAINED' named entity 'ADVANTAGE' named entity 'HAVE' named entity 'LOGISTIC REGRESSION' named entity '80%' named entity 'ESTIMATION'

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