title: External adjustment sensitivity analysis for unmeasured confounding: an application to coronary stent outcomes, Pennsylvania 2004-2008.
abstract: BACKGROUND
  Assessing the real-world comparative effectiveness of common interventions is challenged by unmeasured confounding.
  
  
  OBJECTIVE
  To determine whether the mortality benefit shown for drug-eluting stents (DES) over bare metal stents (BMS) in observational studies persists after controls for/tests for confounding.
  
  
  METHODS
  Retrospective observational study involving 38,019 patients, 65 years or older admitted for an index percutaneous coronary intervention receiving DES or BMS in Pennsylvania in 2004-2005 followed up for death through 3 years.
  
  
  METHODS
  Analysis was at the patient level. Mortality was analyzed with Cox proportional hazards models allowing for stratification by disease severity or DES use propensity, accounting for clustering of patients. Instrumental variables analysis used lagged physician stent usage to proxy for the focal stent type decision. A method originating in work by Cornfield and others in 1954 and popularized by Greenland in 1996 was used to assess robustness to confounding.
  
  
  RESULTS
  DES was associated with a significantly lower adjusted risk of death at 3 years in Cox and in instrumented analyses. An implausibly strong hypothetical unobserved confounder would be required to fully explain these results.
  
  
  CONCLUSIONS
  Confounding by indication can bias observational studies. No strong evidence of such selection biases was found in the reduced risk of death among elderly patients receiving DES instead of BMS in a Pennsylvanian state-wide population.
authors.affiliation: USC Schaeffer Center for Health Policy and Economics, Los Angeles, CA, USA.
authors.forename: Marco D
authors.initials: MD
authors.lastname: Huesch
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D015986:Confounding Factors, Epidemiologic; D054855:Drug-Eluting Stents / Q000009:adverse effects / Q000706:statistics & numerical data; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data; D010414:Pennsylvania; D016016:Proportional Hazards Models; D012189:Retrospective Studies; D012720:Severity of Illness Index; D015607:Stents / Q000009:adverse effects* / Q000706:statistics & numerical data*; D016896:Treatment Outcome; D014481:United States