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Titolo:
Utilization of multiple imperfect assessments of the dependent variable ina logistic regression analysis
Autore:
Magder, LS; Sloan, MA; Duh, SH; Abate, JF; Kittner, SJ;
Indirizzi:
Univ Maryland, Sch Med, Dept Epidemiol & Prevent Med, Baltimore, MD 21201 USA Univ Maryland Baltimore MD USA 21201 Prevent Med, Baltimore, MD 21201 USA Harbin Clin, Dept Neurosci, Rome, GA USA Harbin Clin Rome GA USAHarbin Clin, Dept Neurosci, Rome, GA USA Univ Maryland, Sch Med, Dept Neurol, Baltimore, MD 21201 USA Univ Maryland Baltimore MD USA 21201 Dept Neurol, Baltimore, MD 21201 USA
Titolo Testata:
STATISTICS IN MEDICINE
fascicolo: 1, volume: 19, anno: 2000,
pagine: 99 - 111
SICI:
0277-6715(20000115)19:1<99:UOMIAO>2.0.ZU;2-I
Fonte:
ISI
Lingua:
ENG
Soggetto:
EM ALGORITHM;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Life Sciences
Citazioni:
8
Recensione:
Indirizzi per estratti:
Indirizzo: Magder, LS Univ Maryland, Sch Med, Dept Epidemiol & Prevent Med, 660 W Redwood St, Baltimore, MD 21201 USA Univ Maryland 660 W Redwood St Baltimore MD USA 21201 21201 USA
Citazione:
L.S. Magder et al., "Utilization of multiple imperfect assessments of the dependent variable ina logistic regression analysis", STAT MED, 19(1), 2000, pp. 99-111

Abstract

Often, in biomedical research, there are multiple sources of imperfect information regarding a dichotomous variable of interest. For example, in a study we are conducting on the relationship between cocaine use and stroke risk, information on the cocaine use of each study patient is available from three fallible sources: patient interviews; urine toxicology testing, and medical record review. Regression analyses based on a rule for classifying patients from this information can result in biased estimation of associations and variances due to the misclassification of some subjects and to the assumption of certainty. We describe a likelihood-based method that directlyincorporates multiple sources of information regarding an outcome variableinto a regression analysis and takes into account the uncertainty in the classification. The method can be applied when some sources of information are missing for some subjects. We show how the availability of multiple sources can be exploited to generate estimates of the quality (for example, sensitivity and specificity) of each source and to model the degree to which missing data are informative. A fitting algorithm and issues of identifiability are discussed. We illustrate the method using data from our study. Copyright (C) 2000 John Wiley & Sons, Ltd.

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Documento generato il 04/12/20 alle ore 16:32:16