Conducting Privacy-Preserving Multivariable Propensity Score Analysis When Patient Covariate Information Is Stored in Separate Locations.
Distributed networks of health-care data sources are increasingly being utilized to conduct pharmacoepidemio-logic 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 (separ...
| Publicado en: | American Journal of Epidemiology Vol. 185; no. 6; pp. 501 - 511 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
3/15/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121858164&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121858164 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00029262 1X1 jtl: American Journal of Epidemiology issn: 00029262 maglogo: N pubinfo: dt: 3/15/2017 vid: 185 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 121858164 121858164 121858164 10.1093/aje/kww155 121858164 ppf: 501 ppct: 10 formats: tig: atl: Conducting Privacy-Preserving Multivariable Propensity Score Analysis When Patient Covariate Information Is Stored in Separate Locations. aug: au: Bohn, Justin Eddings, Wesley Schneeweiss, Sebastian affil: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts sug: subj: Privacy and Confidentiality Probability Electronic Health Records Multivariate Analysis Methods Computer Communication Networks Human Epidemiological Research Research Methodology Descriptive Statistics Simulations Clinical Research Algorithms Logistic Regression Drugs, Prescription Adverse Effects Cox Proportional Hazards Model Analysis of Variance Odds Ratio Confidence Intervals Funding Source ab: Distributed networks of health-care data sources are increasingly being utilized to conduct pharmacoepidemio-logic 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 healthcare 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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