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Confounding adjustment via a semi-automated high-dimensional
propensity score algorithm: An application using electronic
medical record data
ISSN
Published in final edited form as: Pharmacoepidemiol Drug Saf.
20
8
849-57
30 de Junio de 2011
Ingles
doi:10.1002/pds.2152.
nihms334273.pdf
Electronic healthcare databases, such as administrative claims and electronic medical record (EMR) databases, are widely used to study the health effects of medical products.1,2 Because most of these databases are not compiled for research purposes, data on some important confounders may not be recorded. As a result, observational studies that use electronic healthcare databases are often criticized for their inability to control for confounding bias.1–3
Purpose—A semi-automated high-dimensional propensity score (hd-PS) algorithm has been proposed to adjust for confounding in claims databases. The feasibility of using this algorithm in other types of healthcare databases is unknown.
Confundir; Bases de datos; Farmacoepidemiología; Análisis del puntaje de propensión.
Confounding; Databases; Pharmacoepidemiology; Propensity score analysis; THIN
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1department Of Population Medicine, Harvard Medical School/harvard Pilgrim Health Care Institute, Boston, Ma

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