title: Conducting Privacy-Preserving Multivariable Propensity Score Analysis When Patient Covariate Information Is Stored in Separate Locations.
abstract: Distributed networks of health-care data sources are increasingly being utilized to conduct pharmacoepidemiologic database studies. Such networks may contain data that are not physically pooled but instead are distributed horizontally (separate patients within each data source) or vertically (separate measures within each data source) in order to preserve patient privacy. While multivariable methods for the analysis of horizontally distributed data are frequently employed, few practical approaches have been put forth to deal with vertically distributed health-care databases. In this paper, we propose 2 propensity score-based approaches to vertically distributed data analysis and test their performance using 5 example studies. We found that these approaches produced point estimates close to what could be achieved without partitioning. We further found a performance benefit (i.e., lower mean squared error) for sequentially passing a propensity score through each data domain (called the "sequential approach") as compared with fitting separate domain-specific propensity scores (called the "parallel approach"). These results were validated in a small simulation study. This proof-of-concept study suggests a new multivariable analysis approach to vertically distributed health-care databases that is practical, preserves patient privacy, and warrants further investigation for use in clinical research applications that rely on health-care databases.
authors.affiliation: Department of Education and Psychology, Free University Berlin, Germany.
authors.forename: Justin
authors.initials: J
authors.lastname: Bohn
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, MA, USA|Harvard Medical School, Boston, MA, USA.
authors.forename: Wesley
authors.initials: W
authors.lastname: Eddings
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, MA, USA|Harvard Medical School, Boston, MA, USA.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schneeweiss
mesh_terms: D035843:Biomedical Research / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data; D003198:Computer Simulation; D003219:Confidentiality; D003627:Data Interpretation, Statistical; D019992:Databases as Topic / Q000706:statistics & numerical data; D016208:Databases, Factual / Q000592:standards; D006801:Humans; D016015:Logistic Models; D008498:Medical Record Linkage / Q000379:methods / Q000592:standards*; D017891:Pharmacoepidemiology / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data; D018907:Privacy; D057216:Propensity Score
keywords: database linkage; database studies; databases; epidemiologic methods; pharmacoepidemiology; propensity scores