title: Multivariable confounding adjustment in distributed data networks without sharing of patient-level data.
abstract: OBJECTIVE
  It is increasingly necessary to analyze data from multiple sources when conducting public health safety surveillance or comparative effectiveness research. However, security, privacy, proprietary, and legal concerns often reduce data holders' willingness to share highly granular information. We describe and compare two approaches that do not require sharing of patient-level information to adjust for confounding in multi-site studies.
  
  
  METHODS
  We estimated the risks of angioedema associated with angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), and aliskiren in comparison with beta-blockers within Mini-Sentinel, which has created a distributed data system of 18 health plans. To obtain the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs), we performed (i) a propensity score-stratified case-centered logistic regression analysis, a method identical to a stratified Cox regression analysis but needing only aggregated risk set data, and (ii) an inverse variance-weighted meta-analysis, which requires only the site-specific HR and variance. We also performed simulations to further compare the two methods.
  
  
  RESULTS
  Compared with beta-blockers, the adjusted HR was 3.04 (95% CI: 2.81, 3.27) for ACEIs, 1.16 (1.00, 1.34) for ARBs, and 2.85 (1.34, 6.04) for aliskiren in the case-centered analysis. The corresponding HRs were 2.98 (2.76, 3.21), 1.15 (1.00, 1.33), and 2.86 (1.35, 6.04) in the meta-analysis. Simulations suggested that the two methods may produce different results under certain analytic scenarios.
  
  
  CONCLUSIONS
  The case-centered analysis and the meta-analysis produced similar results without the need to share patient-level data across sites in our empirical study, but may provide different results in other study settings.
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.
authors.forename: Sengwee
authors.initials: S
authors.lastname: Toh
authors.forename: Marsha E
authors.initials: ME
authors.lastname: Reichman
authors.forename: Monika
authors.initials: M
authors.lastname: Houstoun
authors.forename: Xiao
authors.initials: X
authors.lastname: Ding
authors.forename: Bruce H
authors.initials: BH
authors.lastname: Fireman
authors.forename: Eric
authors.initials: E
authors.lastname: Gravel
authors.forename: Mark
authors.initials: M
authors.lastname: Levenson
authors.forename: Lingling
authors.initials: L
authors.lastname: Li
authors.forename: Erick
authors.initials: E
authors.lastname: Moyneur
authors.forename: Azadeh
authors.initials: A
authors.lastname: Shoaibi
authors.forename: Gwen
authors.initials: G
authors.lastname: Zornberg
authors.forename: Sean
authors.initials: S
authors.lastname: Hennessy
mesh_terms: D000319:Adrenergic beta-Antagonists / Q000009:adverse effects; D000577:Amides / Q000009:adverse effects; D000799:Angioedema / Q000139:chemically induced* / Q000453:epidemiology; D057911:Angiotensin Receptor Antagonists / Q000009:adverse effects; D000806:Angiotensin-Converting Enzyme Inhibitors / Q000009:adverse effects; D057186:Comparative Effectiveness Research / Q000379:methods*; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D016208:Databases, Factual; D005650:Fumarates / Q000009:adverse effects; D006801:Humans; D016015:Logistic Models; D015999:Multivariate Analysis; D017891:Pharmacoepidemiology / Q000379:methods*; D057216:Propensity Score
chemical_list: D000319:Adrenergic beta-Antagonists; D000577:Amides; D057911:Angiotensin Receptor Antagonists; D000806:Angiotensin-Converting Enzyme Inhibitors; D005650:Fumarates; C446481:aliskiren
keywords: Mini-Sentinel; active surveillance; confounding; disease risk scores; distributed data network; pharmacoepidemiology; propensity scores