Back to Search
Start Over
Conducting Privacy-Preserving Multivariable Propensity Score Analysis When Patient Covariate Information Is Stored in Separate Locations.
- Source :
- American Journal of Epidemiology; 3/15/2017, Vol. 185 Issue 6, p501-510, 10p
- Publication Year :
- 2017
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- ALGORITHMS
ANALYSIS of variance
CLINICAL medicine research
COMPUTER networks
CONFIDENCE intervals
DRUGS
EPIDEMIOLOGICAL research
RESEARCH methodology
MEDICAL ethics
MULTIVARIATE analysis
PRIVACY
PROBABILITY theory
RESEARCH funding
LOGISTIC regression analysis
PROPORTIONAL hazards models
ELECTRONIC health records
DESCRIPTIVE statistics
ODDS ratio
Subjects
Details
- Language :
- English
- ISSN :
- 00029262
- Volume :
- 185
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- American Journal of Epidemiology
- Publication Type :
- Academic Journal
- Accession number :
- 121858164
- Full Text :
- https://doi.org/10.1093/aje/kww155