Multivariable Confounding Adjustment in Distributed Data Networks without Sharing of Patient-Level Data

Project Title Multivariable Confounding Adjustment in Distributed Data Networks without Sharing of Patient-Level Data
Date
Monday, July 22, 2013
Location
Description

This article describes two approaches to conducting multivariable analyses for multi-site studies without sharing of patient-level data across sites: the case-centered logistic regression approach and the inverse variance-weighted meta-analysis approach.  The authors evaluate the above methods 1) by analyzing risks of angioedema associated with use of angiotensin-converting enzyme inhibitors, antiotensin II receptor blockers, and aliskiren, compared to use of beta-blockers in the Mini-Sentinel Distributed Database (MSDD), and 2) by performing simulations.

Medical Product
aliskiren
angiotensin II receptor blocker (ARB)
angiotensin-converting enzyme (ACE) inhibitor
Health Outcome
angioedema
Time Period
2001-2010
Corresponding Author

S. Toh, ScD, Department of Population Medicine, Harvard Pilgrim Healthcare Institute and Harvard Medical School, 401 Park Drive, Suite 401 East, Boston, MA 02215, USA. Email: darren_toh@harvardpilgrim.org

Authors

Sengwee Toh ScD; Marsha E. Reichman PhD; Monika Houstoun PharmD; Xiao Ding PhD; Bruce H. Fireman MA; Eric Gravel; Mark Levenson PhD; Lingling Li PhD; Erick Moyneur; Azadeh Shoaibi PhD, MHS; Gwen Zornberg MD, MS, ScD; Sean Hennessy PharmD, PhD 

Data Sources
Mini-Sentinel Distributed Database (MSDD)
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