title: Measuring the Volume-Outcome Relation for Complex Hospital Surgery.
abstract: BACKGROUND
  Prominent studies continue to measure the hospital volume-outcome relation using simple logistic or random-effects models. These regression models may not appropriately account for unobserved differences across hospitals (such as differences in organizational effectiveness) which could be mistaken for a volume outcome relation.
  
  
  OBJECTIVE
  To explore alternative estimation methods for measuring the volume-outcome relation for six major cancer operations, and to determine which estimation method is most appropriate.
  
  
  METHODS
  We analyzed patient-level hospital discharge data from three USA states and data from the American Hospital Association Annual Survey of Hospitals from 2000 to 2011. We studied six major cancer operations using three regression frameworks (logistic, fixed-effects, and random-effects) to determine the correlation between patient outcome (mortality) and hospital volume.
  
  
  RESULTS
  For our data, logistic and random-effects models suggest a non-zero volume effect, whereas fixed-effects models do not. Model-specification tests support the fixed-effects or random-effects model, depending on the surgical procedure; the basic logistic model is always rejected. Esophagectomy and rectal resection do not exhibit significant volume effects, whereas colectomy, pancreatic resection, pneumonectomy, and pulmonary lobectomy do.
  
  
  CONCLUSIONS
  The statistical significance of the hospital volume-outcome relation depends critically on the regression model. A simple logistic model cannot control for unobserved differences across hospitals that may be mistaken for a volume effect. Even when one applies panel-data methods, one must carefully choose between fixed- and random-effects models.
authors.affiliation: Department of Economics MS-22, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
authors.forename: Woohyeon
authors.initials: W
authors.lastname: Kim
authors.affiliation: Department of Mathematics MS-136, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
authors.forename: Stephen
authors.initials: S
authors.lastname: Wolff
authors.affiliation: Department of Economics MS-22, Rice University, 6100 Main Street, Houston, TX, 77005, USA. vho@rice.edu.|Baker Institute for Public Policy, Rice University, 6100 Main Street MS 40, Houston, TX, 77005, USA. vho@rice.edu.|Department of Medicine, Baylor College of Medicine, Houston, TX, USA. vho@rice.edu.
authors.forename: Vivian
authors.initials: V
authors.lastname: Ho
mesh_terms: D000328:Adult; D000368:Aged; D000573:American Hospital Association; D005260:Female; D005431:Florida / Q000453:epidemiology; D017052:Hospital Mortality; D061847:Hospitals, High-Volume / Q000706:statistics & numerical data*; D061846:Hospitals, Low-Volume / Q000706:statistics & numerical data*; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009369:Neoplasms / Q000145:classification / Q000401:mortality / Q000601:surgery*; D009515:New Jersey / Q000453:epidemiology; D009518:New York / Q000453:epidemiology; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D010351:Patient Discharge / Q000592:standards / Q000706:statistics & numerical data*; D012044:Regression Analysis; D013514:Surgical Procedures, Operative / Q000145:classification / Q000401:mortality / Q000706:statistics & numerical data; D014481:United States / Q000453:epidemiology