Statistical issues in reporting quality data: small samples and casemix variation.
OBJECTIVE
To present two key statistical issues that arise in analysis and reporting of quality data.


CONCLUSIONS
Casemix variation is relevant to quality reporting when the units being measured have differing distributions of patient characteristics that also affect the quality outcome. When this is the case, adjustment using stratification or regression may be appropriate. Such adjustments may be controversial when the patient characteristic does not have an obvious relationship to the outcome. Stratified reporting poses problems for sample size and reporting format, but may be useful when casemix effects vary across units. Although there are no absolute standards of reliability, high reliabilities (interunit F > or = 10 or reliability > or = 0.9) are desirable for distinguishing above- and below-average units. When small or unequal sample sizes complicate reporting, precision may be improved using indirect estimation techniques that incorporate auxiliary information, and 'shrinkage' estimation can help to summarize the strength of evidence about units with small samples.


CONCLUSIONS
With broader understanding of casemix adjustment and methods for analyzing small samples, quality data can be analysed and reported more accurately.
Zaslavsky
A M
AM
Department of Health Care Policcy, Harvard Medical School, Boston, MA 02115-5899, USA. zaslavsk@hcp.med.harvard.edu
D003627:Data Interpretation, Statistical; D003953:Diagnosis-Related Groups; D006801:Humans; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data*; D018401:Sample Size