The use of fixed- and random-effects models for classifying hospitals as mortality outliers: a Monte Carlo assessment.
BACKGROUND
There is an increasing movement towards the release of hospital "report-cards. "However, there is a paucity of research into the abilities of the different methods to correctly classify hospitals as performance outliers.


OBJECTIVE
To examine the ability of risk-adjusted mortality rates computed using conventional logistic regression and random-effects logistic regression models to correctly identify hospitals that have higher than acceptable mortality.


METHODS
Monte Carlo simulations.


METHODS
Sensitivity, specificity, and positive predictive value of a classification as a high-outlier for identifying hospitals with higher than acceptable mortality rates.


RESULTS
When the distribution of hospital specific log-odds of death was normal, random-effects models had greater specificity and positive predictive value than fixed-effects models. However, fixed-effects models had greater sensitivity than random-effects models.


CONCLUSIONS
Researchers and policy makers need to carefully consider the balance between false positives and false negatives when choosing statistical models for determining which hospitals have higher than acceptable mortality in performance profiling.
Austin
Peter C
PC
Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. peter.austin@ices.on.ca
Alter
David A
DA
Tu
Jack V
JV
D017052:Hospital Mortality; D006761:Hospitals / Q000145:classification* / Q000592:standards; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D009203:Myocardial Infarction / Q000150:complications / Q000401:mortality; D009864:Ontario; D017063:Outcome Assessment, Health Care / Q000379:methods*; D016311:Outliers, DRG; D020379:Risk Adjustment / Q000379:methods*; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index