Geistanger, Andrea Ulrike

Eldorado - Ressourcen aus und für Lehre, Studium und Forschung

AbstractStatistical Methods for the Standardization of DiagnosticAssaysDissertation zur Erlangung des Grades eines Doktors der NaturwissenschaftenFachbereich Statistik, Universita¨t DortmundAndrea Ulrike GeistangerDiagnostic assays are measurement systems, measuring the concentration of analytes inhuman body liquids. To ensure the stability of the measured values over time, each diagnosticassays should be standardized against a so-called master sample. This is a sample with knownconcentration, which is measured by a very specific and precise measurement method. Fromthis master copies are made subsequently, such that at the end of the chain a patient sample ismeasured 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 wellas international organizations, especially the IFCC1 have recognized the need for standardiza-tion systems of diagnostic assays that ensure stability.Networks of laboratories are formed, which measure master samples with a reference measure-ment 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 thenext master sample, such that if the network is stable the continuity of master samples will beguaranteed.In the context of such laboratory networks several statistical questions arise, which are dis-cussed and answered throughout this thesis.First it must be clear how the assigned value of the respective master and the uncertaintyassociated with this value is derived. The first part of the thesis examines a routine processof standardization within a laboratory network. The main sources of uncertainty within thisprocess are revealed and how these sources have to be combined to obtain the uncertainty ofthe assigned value is shown. Especially the question how the uncertainty of the master is trans-ferred to the uncertainty of the copies is discussed. A Bayesian model is presented whichenables the inclusion of the uncertainty of the master within the calibration process. Based ona simulation study it is shown that this model leads to much better results for the estimation ofa measured value as well as its uncertainty, than the conventional calibration models. Further itis shown how so derived measured values should be combined to obtain the assigned value andits uncertainty.The second part is dedicated to the identification of outliers in data of standardization net-works. This is important for two reasons: one reason is that new laboratories may want to join1International Federation of Clinical Chemistrya standardization network. As the network should ensure the stability of the master, the newlaboratory must fit to the network. The other reason is that failures of measurements of themembers of the network need to be detected before the assigned value is calculated. For bothquestions rules have to be defined which are valid for multiple value assignments.The outlier identification for both tasks is based on robust estimation methods for linear mixedmodels. First it is shown how outlier identification rules for general linear mixed models can bedefined. Afterwards two special cases, the one-way random effects model and the random coef-ficients model are regarded. Both are useful for the analysis of data from laboratory networks,the first one if only one sample within the network is regarded, the second if multiple samplesare of interest. Finally an interpretation of the impact of these rules for allowable measurementdeviations within a laboratory network is given.The third part of the thesis deals with repeated method comparison studies which are nec-essary, if a standardization system is replaced by a new one. This might happen if globalstandardization system replace existing national systems, or if a more specific measurementmethod is established. In these cases, assigned values might change and recalculation formulasare needed for the transformation of new values into old ones and vice versa. Concepts arepresented for the comparison of repeated method comparison studies.Further the combination of these studies via hierarchical Bayesian models, to obtain a recalcu-lation formula, is presented. The focus lies especially on the impact of the prior distributionson the results and the definition of appropriate prior distributions based on posterior predictivechecks.The presented statistical methods are applied to data of the IFCC network for standardiza-tion of HbA1c.

Statistical methods for the standardization of diagnostic assays

https://eldorado.tu-dortmund.de/bitstream/2003/24527/1/AbstractGeistanger.pdf

Statistical methods for the standardization of diagnostic assays

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