title: Confounding adjustment in comparative effectiveness research conducted within distributed research networks.
abstract: BACKGROUND
  A distributed research network (DRN) of electronic health care databases, in which data reside behind the firewall of each data partner, can support a wide range of comparative effectiveness research (CER) activities. An essential component of a fully functional DRN is the capability to perform robust statistical analyses to produce valid, actionable evidence without compromising patient privacy, data security, or proprietary interests.
  
  
  OBJECTIVE
  We describe the strengths and limitations of different confounding adjustment approaches that can be considered in observational CER studies conducted within DRNs, and the theoretical and practical issues to consider when selecting among them in various study settings.
  
  
  RESULTS
  Several methods can be used to adjust for multiple confounders simultaneously, either as individual covariates or as confounder summary scores (eg, propensity scores and disease risk scores), including: (1) centralized analysis of patient-level data, (2) case-centered logistic regression of risk set data, (3) stratified or matched analysis of aggregated data, (4) distributed regression analysis, and (5) meta-analysis of site-specific effect estimates. These methods require different granularities of information be shared across sites and afford investigators different levels of analytic flexibility.
  
  
  CONCLUSIONS
  DRNs are growing in use and sharing of highly detailed patient-level information is not always feasible in DRNs. Methods that incorporate confounder summary scores allow investigators to adjust for a large number of confounding factors without the need to transfer potentially identifiable information in DRNs. They have the potential to let investigators perform many analyses traditionally conducted through a centralized dataset with detailed patient-level information.
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA. darrentoh@post.harvard.edu
authors.forename: Sengwee
authors.initials: S
authors.lastname: Toh
authors.forename: Joshua J
authors.initials: JJ
authors.lastname: Gagne
authors.forename: Jeremy A
authors.initials: JA
authors.lastname: Rassen
authors.forename: Bruce H
authors.initials: BH
authors.lastname: Fireman
authors.forename: Martin
authors.initials: M
authors.lastname: Kulldorff
authors.forename: Jeffrey S
authors.initials: JS
authors.lastname: Brown
mesh_terms: D057186:Comparative Effectiveness Research / Q000458:organization & administration*; D016494:Computer Security; D003219:Confidentiality; D015986:Confounding Factors, Epidemiologic; D006801:Humans; D016015:Logistic Models; D008491:Medical Informatics Applications; D015337:Multicenter Studies as Topic / Q000379:methods*; D057216:Propensity Score; D012107:Research Design