Implementation of multivariate control charts in a clinical setting.

BACKGROUND In most clinical monitoring cases there is a need to track more than one quality characteristic. If separate univariate charts are used, the overall probability of a false alarm may be inflated since correlation between variables is ignored. In such cases, multivariate control charts should be considered. PURPOSE This paper considers the implementation and performance of the T(2), multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) charts in light of the challenges faced in clinical settings. We discuss how to handle incomplete records and non-normality of data, and we provide recommendations on chart selection. DATA SOURCES Our discussion is supported by a case study involving the monitoring of radiation delivered to patients undergoing diagnostic coronary angiogram procedures at St Andrew's War Memorial Hospital, Australia. We also perform a simulation study to investigate chart performance for various correlation structures, patterns of mean shifts, amounts of missing data and methods of imputation. CONCLUSIONS The MEWMA and MCUSUM charts detect small to moderate shifts quickly, even when the quality characteristics are uncorrelated. The T(2) chart performs less well overall, although it is useful for rapid detection of large shifts. When records are incomplete, we recommend using multiple imputation.