New control chart for multivariate data with missing values

A new multivariate statistical quality control method has been developed. It is an extension of the method developed by Kume, which is able to find abnormal values in multivariate biochemical data of a clinical laboratory. The present method makes use of the difference between two sets of data measured from the samples of the same patient obtained on different days. The Mahalanobis' distance between two samples can be calculated from the difference of their observations. If the Mahalanobis' distance of the two data is larger than the critical value decided in advance, the reliability of the measurement is doubtful. The characteristic of the present method is that it can apply to data with missing values by estimating them from measured data. Some numerical examples are shown to demonstrate the availability of the method.