title: Comparing hierarchical modeling with traditional logistic regression analysis among patients hospitalized with acute myocardial infarction: should we be analyzing cardiovascular outcomes data differently?
abstract: BACKGROUND
  Data in health research are frequently structured hierarchically. For example, data may consist of patients treated by physicians who in turn practice in hospitals. Traditional statistical techniques ignore the possible correlation of outcomes within a given practice or hospital. Furthermore, imputing characteristics measured at higher levels of the hierarchy to the patient-level artificially inflates the amount of available information on the effect of higher-level characteristics on outcomes.
  
  
  METHODS
  Conventional logistic regression models and multilevel logistic regression models were fit to a cross-sectional cohort of patients hospitalized with a diagnosis of acute myocardial infarction. The statistical significance of the effect of patient, physician, and hospital characteristics on patient outcomes was compared between the 2 modeling strategies.
  
  
  RESULTS
  The 2 analytic strategies agreed well on the effect of patient characteristics on outcomes. According to the traditional analysis, teaching status was statistically significantly associated with 5 of the 9 outcomes, whereas the multilevel models did not find a statistically significant association between teaching status and any patient outcomes. Similarly, the traditional and multilevel models disagreed on the statistical significance of the effect of being treated at a revascularization hospital and 3 patient outcomes.
  
  
  CONCLUSIONS
  In comparing the resultant models, we see that false inferences can be drawn by ignoring the structure of the data. Conventional logistic regression tended to increase the statistical significance for the effects of variables measured at the hospital-level compared to the level of significance indicated by the multilevel model.
authors.affiliation: Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. peter.austin@;ices.on.ca
authors.forename: Peter C
authors.initials: PC
authors.lastname: Austin
authors.forename: Jack V
authors.initials: JV
authors.lastname: Tu
authors.forename: David A
authors.initials: DA
authors.lastname: Alter
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D009203:Myocardial Infarction / Q000401:mortality* / Q000628:therapy; D009864:Ontario / Q000453:epidemiology; D012720:Severity of Illness Index; D017678:Sex Distribution; D015996:Survival Rate; D016896:Treatment Outcome