Risk management for analytical methods based on the total error concept:Conciliating the objectives of the pre-study and in-study validation phases

Bruno Boulanger (1) , Walthère Dewé (1) , Aurélie Gilbert (2) , Bernadette Govaerts (2) , Myriam Maumy (3)

1 EEPS - European Early Phase Statistics
2 Institut de Statistique, Biostatistique et Sciences Actuarielles (ISBA)
3 IRMA - Institut de Recherche Mathématique Avancée

In industries that involve either chemistry or biology, the analytical methods are the necessary eyes of all the material produced. If the quality of an analytical method is doubtful, then the whole set of decisions that will be based on those measures is questionable. For those reasons, being able to assess the quality of an analytical method is far more than a statistical challenge; it's a matter of ethic and good business practices. The validity of an analytical method must be assessed at two levels. The “pre-study” validation aims at proving, by an appropriate set of designed experiments, that the method is able to achieve its objectives. The “in-study” validation is intended to verify, by inserting QC samples in routine, that the method remains valid over time. This paper discusses and compares two methods, based on the total error concept, to check the validity of a measurement method at a pre-study level. The first checks if a tolerance interval on hypothetical future measurements lies within given acceptance limits and the second calculates the probability to lie within these limits and verifies if it is greater than a given acceptance level. For the “in-study” validation, the paper assesses the properties of the s-n-l rule recommended by FDA. A crucial point is also to ensure that the decisions taken at the pre-study stage and in routine are compatible. More precisely, a laboratory should not see its method rejected in routine when it has been proven to be valid and remains so. This goal may be achieved by an appropriate choice of validation parameters at both pre- and in-study levels.

Domaines: Statistiques [math.ST]

Bruno Boulanger, Walthère Dewé, Aurélie Gilbert, Bernadette Govaerts, Myriam Maumy. Risk management for analytical methods based on the total error concept:Conciliating the objectives of the pre-study and in-study validation phases. Chemometrics and Intelligent Laboratory Systems, 2007, 86, pp.198-207. ⟨10.1016/j.chemolab.2006.06.008⟩. ⟨hal-00141709⟩