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International Journal for Quality in Health Care 13:481-488 (2001)
© 2001 International Society for Quality in Health Care

Statistical issues in reporting quality data: small samples and casemix variation

Alan M. Zaslavsky

Department of Health Care Policy, Harvard Medical School, Boston, USA

Purpose. To present two key statistical issues that arise in analysis and reporting of quality data.

Summary. 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 >= 10 or reliability >= 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.

Keywords: casemix adjustment, hierarchical models, quality measurement, quality reporting, regression, significance tests, stratification


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