Reference Ranges and Control Limits that are Resistant to Baseline Outliers

Reference ranges and control limits are used in many settings – for example, to assess a person’s health or to monitor the stability of a manufacturing process. Such ranges are established based on a baseline sample of what is considered normal data, but it is not possible to always avoid a few outliers being present even in this sample. If, as is common, the range is calculated using statistics, such as the mean and standard deviation, which could be influenced by outliers, then the use of such a range could adversely affect the decisions made. This can be avoided by constructing the reference range using statistics that are resistant to outliers. In this paper, we studied possible approaches and found two methods that had superior performance overall: one based on MM-estimation and one based on a form of Winsorization.