A new proposal for the detection of laboratories that provide not consistent results in an ILS is based on functional data analysis techniques, random projections and the functional extensions of Mandel’s h and k statistics. Based on these statistics, the approach to contrast the Repeatability and Reproducibility Hypotheses for the functional case will be carried out. The key idea of this new methodology is to transfer the resolution of a functional problem to a simpler case of study, the real case.
In addition, a case study is presented on clinical laboratories using Bootstrap resampling to detect laboratories providing inconsistent results in an ILS. Traditionally, in an ILS it is assumed that laboratory data follow the normal distribution. However, in this degree work they develop an algorithm where they use non-parametric Bootstrap resampling to calculate the critical values of Mandel’s h and k statistics to detect inconsistent results in an ILS.
Both methodologies are presented in the degree theses of the students of the National Polytechnic School and the University of the Armed Forces respectively in detail below.
New methodology for the detection of not consistent results in interlaboratory studies using functional data analysis
Cite
Solorzano Maya, C. I., & Moreno Armijos, G. C. (2022). New methodology for the detection of inconsistent results in interlaboratory studies using functional data analysis (Bachelor’s thesis, Quito: EPN, 2022).
@mastersthesis{moreno2022nueva,
title={Nueva metodolog{\'\i}a para la detecci{\'o}n de resultados inconsistentes en estudios interlaboratorios utilizando an{\'a}lisis de datos funcionales.},
author={Moreno Armijos, G{\'e}nesis Chabeli},
type={{B.S.} thesis},
year={2022},
school={Quito: EPN, 2022.}
}