A Comparison of Multivariate Outlier Detection Methods for Clinical Laboratory Safety Data

During a clinical trial of a new treatment, a large number of variables are measured to monitor the safety of the treatment. It is important to detect outlying observations which may indicate that something abnormal is happening. To do this effectively, techniques are needed for finding multivariate outliers. Six techniques of this sort are described and illustrated on a typical laboratory safety data set. Their properties are investigated more thoroughly by means of a simulation study. The results show that some methods do better than others depending on whether or not the data set is multivariate normal, the dimension of the data set, the type of outlier, the proportion of outliers in a data set and the degree of contamination, i.e. `outlyingness'. The results indicate that it is desirable to run a battery of multivariate methods on a particular data set in an attempt to highlight possible outliers.