Outlier detection for a hierarchical Bayes model in a study of hospital variation in surgical procedures.
One of the most important aspects of profiling healthcare providers or services is constructing a model that is flexible enough to allow for random variation. At the same time, we wish to identify those institutions that clearly deviate from the usual standard of care. Here, we propose a hierarchical Bayes model to study the choice of surgical procedure for rectal cancer using data previously analysed by Simons et al.(1) Using hospitals as random effects, we construct a computationally simple graphical method for determining hospitals that are outliers; that is, they differ significantly from other hospitals of the same type in terms of surgical choice.
School of Mathematics and Statistics, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, Canada. pfarrell@math.carleton.ca
Patrick J
PJ
Farrell
Susan
S
Groshen
Brenda
B
Macgibbon
Thomas J
TJ
Tomberlin
D001499:Bayes Theorem; D056808:Biostatistics; D003627:Data Interpretation, Statistical; D013505:Digestive System Surgical Procedures / Q000592:standards / Q000706:statistics & numerical data; D006761:Hospitals / Q000592:standards* / Q000706:statistics & numerical data*; D006801:Humans; D015141:Los Angeles; D015233:Models, Statistical; D012004:Rectal Neoplasms / Q000601:surgery; D012042:Registries; D018426:SEER Program; D059039:Standard of Care / Q000706:statistics & numerical data; D013514:Surgical Procedures, Operative / Q000592:standards* / Q000706:statistics & numerical data*