title: Examination of the treatment selection process in a multicenter observational study.
abstract: BACKGROUND
  Many multicenter clinical trials use permuted-block randomization to create balanced treatment allocations within clinical centers. Unlike randomized trials, observational studies do not control treatment allocation, and statistical models are used to adjust for measured confounders. For many observational data analyses, the variability in the treatment selection process within clinical centers is ignored. Furthermore, there is no consensus on the best approach for dealing with variability in the treatment selection process across clinical centers.
  
  
  RESULTS
  Individuals aged ≥65 years receiving either drug-eluting stents or bare metal stents were included. A cohort of 262 700 patients from 650 CathPCI Registry sites was followed up for a median of 15 months. Propensity score models were estimated to describe the process used to select drug-eluting stents across the study population. Substantial variability in the use of drug-eluting stents at the clinical center level was observed-even after accounting for differences in patient and clinical center characteristics. By refitting and matching propensity scores within clinical centers, a balanced cohort on treatment allocation and prognostic factors was obtained. This approach generated an estimated hazard ratio that was qualitatively similar to standard regression models and other propensity score approaches.
  
  
  CONCLUSIONS
  Substantial variability in treatment selection existed between clinical centers. Matching recalibrated propensity scores within clinical centers has the potential to reduce a source of bias in multicenter observational studies. This methodology cannot eliminate all potential for biases; however, it removes the potential bias from site-level factors.
authors.affiliation: From the Department of Biostatistics and Bioinformatics (K.J.A.), Department of Medicine (J.M.B., E.L.E., E.D.P., P.S.D.), Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.J.F., D.D.); and Department of Health Policy and Management, UNC Gillings School of Global Public Health, University of North Carolina School of Medicine, Chapel Hill (J.J.F.). kevin.anstrom@duke.edu.
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: Anstrom
authors.affiliation: From the Department of Biostatistics and Bioinformatics (K.J.A.), Department of Medicine (J.M.B., E.L.E., E.D.P., P.S.D.), Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.J.F., D.D.); and Department of Health Policy and Management, UNC Gillings School of Global Public Health, University of North Carolina School of Medicine, Chapel Hill (J.J.F.).
authors.forename: J Matthew
authors.initials: JM
authors.lastname: Brennan
authors.affiliation: From the Department of Biostatistics and Bioinformatics (K.J.A.), Department of Medicine (J.M.B., E.L.E., E.D.P., P.S.D.), Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.J.F., D.D.); and Department of Health Policy and Management, UNC Gillings School of Global Public Health, University of North Carolina School of Medicine, Chapel Hill (J.J.F.).
authors.forename: Eric L
authors.initials: EL
authors.lastname: Eisenstein
authors.affiliation: From the Department of Biostatistics and Bioinformatics (K.J.A.), Department of Medicine (J.M.B., E.L.E., E.D.P., P.S.D.), Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.J.F., D.D.); and Department of Health Policy and Management, UNC Gillings School of Global Public Health, University of North Carolina School of Medicine, Chapel Hill (J.J.F.).
authors.forename: Jerome J
authors.initials: JJ
authors.lastname: Federspiel
authors.affiliation: From the Department of Biostatistics and Bioinformatics (K.J.A.), Department of Medicine (J.M.B., E.L.E., E.D.P., P.S.D.), Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.J.F., D.D.); and Department of Health Policy and Management, UNC Gillings School of Global Public Health, University of North Carolina School of Medicine, Chapel Hill (J.J.F.).
authors.forename: David
authors.initials: D
authors.lastname: Dai
authors.affiliation: From the Department of Biostatistics and Bioinformatics (K.J.A.), Department of Medicine (J.M.B., E.L.E., E.D.P., P.S.D.), Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.J.F., D.D.); and Department of Health Policy and Management, UNC Gillings School of Global Public Health, University of North Carolina School of Medicine, Chapel Hill (J.J.F.).
authors.forename: Eric D
authors.initials: ED
authors.lastname: Peterson
authors.affiliation: From the Department of Biostatistics and Bioinformatics (K.J.A.), Department of Medicine (J.M.B., E.L.E., E.D.P., P.S.D.), Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.J.F., D.D.); and Department of Health Policy and Management, UNC Gillings School of Global Public Health, University of North Carolina School of Medicine, Chapel Hill (J.J.F.).
authors.forename: Pamela S
authors.initials: PS
authors.lastname: Douglas
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D003324:Coronary Artery Disease / Q000175:diagnosis* / Q000453:epidemiology* / Q000628:therapy; D054855:Drug-Eluting Stents / Q000706:statistics & numerical data*; D005500:Follow-Up Studies; D006801:Humans; D015588:Observer Variation; D017063:Outcome Assessment, Health Care; D018579:Patient Selection; D062645:Percutaneous Coronary Intervention; D011379:Prognosis; D057216:Propensity Score; D016896:Treatment Outcome; D014481:United States
keywords: comparative effectiveness research; propensity score; selection for treatment