The probability of being identified as an outlier with commonly used funnel plot control limits for the standardised mortality ratio

Abstract

Background: Emphasis is increasingly being placed on the monitoring of clinical outcomes for health care providers. Funnel plots have become an increasingly popular graphical methodology used to identify potential outliers. It is assumed that a provider only displaying expected random variation (i.e. in-control) will fall outside a control limit with a known probability. In reality, the discrete count nature of these data, and the differing methods, can lead to true probabilities quite different from the nominal value. This paper investigates the true probability of an in control provider falling outside control limits for the Standardised Mortality Ratio (SMR). Methods: The true probabilities of an in control provider falling outside control limits for the SMR were calculated and compared for three commonly used limits: Wald confidence interval; exact confidence interval; probabilitybased prediction interval. Results: The probability of falling above the upper limit, or below the lower limit, often varied greatly from the nominal value. This was particularly apparent when there were a small number of expected events: for expected events ≤50 the median probability of an in-control provider falling above the upper 95% limit was 0.0301 (Wald), 0.0121 (exact), 0.0201 (prediction). Conclusions: It is important to understand the properties and probability of being identified as an outlier by each of these different methods to aid the correct identification of poorly performing health care providers. The limits obtained using probability-based prediction limits have the most intuitive interpretation and their properties can be defined a priori. Funnel plot control limits for the SMR should not be based on confidence intervals. © 2012 Seaton and Manktelow; licensee BioMed Central Ltd.