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.


OBJECTIVE
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.


METHODS
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.
Waterhouse
Mary
M
School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia. mary.waterhouse@gmail.com
Smith
Ian
I
Assareh
Hassan
H
Mengersen
Kerrie
K
D006801:Humans; D019982:Organizational Case Studies; D011785:Quality Assurance, Health Care / Q000379:methods*; D058996:Quality Improvement / Q000458:organization & administration