der Universität Dortmund

Abstract

Diagnostic assays are measurement systems, measuring the concentration of analytes in human body liquids. To ensure the stability of the measured values over time, each diagnostic assays should be standardized against a so-called master sample. This is a sample with known concentration, which is measured by a very specific and precise measurement method. From this master copies are made subsequently, such that at the end of the chain a patient sample is measured on the standardized system. A main problem for standardization systems of diagnostic assays is the definition of a master, which is stable, as analyte may be lost over time. Manufacturers of diagnostics assays as well as international organizations, especially the IFCC ∗ have recognized the need for standardization systems of diagnostic assays that ensure stability. Networks of laboratories are formed, which measure master samples with a reference measurement method and the averaged value of these measurements becomes the value of the master, the so-called assigned value. This value assignment is repeated after a certain time span for the next master sample, such that if the network is stable the continuity of master samples will be guaranteed. In the context of such laboratory networks several statistical questions arise, which are discussed and answered throughout this thesis. First it must be clear how the assigned value of the respective master and the uncertainty associated with this value is derived. The first part of the thesis examines a routine process of standardization within a laboratory network. The main sources of uncertainty within this process are revealed and how these sources have to be combined to obtain the uncertainty of the assigned value is shown. Especially the question how the uncertainty of the master is transferred to the uncertainty of the copies is discussed. A Bayesian model is presented which enables the inclusion of the uncertainty of the master within the calibration process. Based on a simulation study it is shown that thi