title: A Bayesian hierarchical approach to comparative audit for carotid surgery.
abstract: OBJECTIVE
  the aim of this study was to illustrate how a Bayesian hierarchical modelling approach can aid the reliable comparison of outcome rates between surgeons.
  
  
  METHODS
  retrospective analysis of prospective and retrospective data.
  
  
  METHODS
  binary outcome data (death/stroke within 30 days), together with information on 15 possible risk factors specific for CEA were available on 836 CEAs performed by four vascular surgeons from 1992-99. The median patient age was 68 (range 38-86) years and 60% were men.
  
  
  METHODS
  the model was developed using the WinBUGS software. After adjusting for patient-level risk factors, a cross-validatory approach was adopted to identify "divergent" performance. A ranking exercise was also carried out.
  
  
  RESULTS
  the overall observed 30-day stroke/death rate was 3.9% (33/836). The model found diabetes, stroke and heart disease to be significant risk factors. There was no significant difference between the predicted and observed outcome rates for any surgeon (Bayesian p -value>0.05). Each surgeon had a median rank of 3 with associated 95% CI 1.0-5.0, despite the variability of observed stroke/death rate from 2.9-4.4%. After risk adjustment, there was very little residual between-surgeon variability in outcome rate.
  
  
  CONCLUSIONS
  Bayesian hierarchical models can help to accurately quantify the uncertainty associated with surgeons' performance and rank.
authors.affiliation: Academic Vascular Unit, Hull Royal Infirmary, Anlaby Road, Hull, HU3 2JZ, UK.
authors.forename: G
authors.initials: G
authors.lastname: Kuhan
authors.forename: E C
authors.initials: EC
authors.lastname: Marshall
authors.forename: A F
authors.initials: AF
authors.lastname: Abidia
authors.forename: I C
authors.initials: IC
authors.lastname: Chetter
authors.forename: P T
authors.initials: PT
authors.lastname: McCollum
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D002340:Carotid Artery Diseases / Q000150:complications / Q000401:mortality / Q000601:surgery*; D002983:Clinical Competence / Q000706:statistics & numerical data*; D016894:Endarterectomy, Carotid / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008485:Medical Audit / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D015996:Survival Rate