Error Methods Are More Practical, But Uncertainty Methods May Still Be Preferred

“Error methods—compared with uncertainty methods—offer simpler, more intuitive and practical procedures for calculating measurement uncertainty and conducting quality assurance in laboratory medicine. However, uncertainty methods are preferred in other fields of sciences as reflected by the guide to the expression of uncertainty in measurement.” (1).

Those are the opening sentences in the report from the Total Error Task and Finish Group (TE-TFG)² of the European Federation of Clinical Chemistry and Laboratory Medicine (1). The TE-TFG was appointed by the European Federation of Clinical Chemistry and Laboratory Medicine as 1 of the outcomes from the 2014 Milan conference on quality specifications (2) to resolve issues regarding total analytical error (TAE) vs measurement uncertainty (MU).

The focus of the 2014 Milan meeting was to review existing recommendations for setting analytical performance specifications and to update the guidelines from the 1999 Stockholm Consensus Conference. One outcome was that the hierarchy of goal-setting models was reduced from 5 to 3 classes—specific models for clinical use, general models related to individual and population variation, and “state-of-the-art” recommendations from expert groups and proficiency testing (PT) and external quality assessment (EQA) programs. Although the PT/EQA criteria for acceptable performance fall into the lowest class, these survey programs are well established globally and require goals for TAE because only a single measurement is allowed on survey samples and that measurement is subject to both random and systematic errors, i.e., precision and trueness. Thus, there is an ongoing need for the TAE model and goals for TAE.