Reference : Contribution to the statistical evaluation of data obtained in External Quality Asses...
Dissertations and theses : Doctoral thesis
Human health sciences : General & internal medicine
Contribution to the statistical evaluation of data obtained in External Quality Assessment programmes
[fr] Contrbution à l'analyse statistique des données obtenues dans les programmes d'Evaluation Extern de la Qualité
Coucke, Wim mailto [Université de Liège - ULiège > > > Doct. sc. santé publ.]
Université de Liège, ​Liège, ​​Belgique
Docteur en Sciences de la Santé publique
Albert, Adelin mailto
CHAPELLE, Jean-Paul mailto
GILLET, Pierre mailto
LIBEER, Jean-Claude
[en] Laboratory medicine has undergone a spectacular evolution in the last decades and has become today of crucial importance for supporting diagnostic and therapeutic decisions. The increase of the volume of laboratory analyses has not gone without an emerging risk of measurement errors that may have far-reaching consequences, even on the patient’s life. External Quality Assessment (EQA), already established since several decades in various countries and often running on an international level, aim at going further than the "internal quality control" procedures of every laboratory and at improving laboratory quality by inter-laboratory comparisons. An EQA round generally consists of sending aliquots of the same sample to various laboratories for assaying selected tests. After finishing the assays, results are reported back to the EQA organizer. Subsequently these results are subject to a statistical analysis, which is performed globally, for all the participants, or for each analytical technique separately. Finally, a report is sent to every participant that informs about the acceptability of the individual results, with respect to predefined limits, and with respect to the group of peers. This thesis, structured in five chapters, focuses on the External Quality Control of clinical laboratories by a critical analysis of existing methods and by creating new approaches that permit to improve the current procedures. The first chapter of this work emphasizes the evolution of the role of the clinical laboratory and EQA in the quality improvement. After the report ’To Err is Human: Building a Safer Health System’, numerous scientists became interested in investigating the frequency, source and impact of laboratory errors. The Total Testing Process (TTP) became recognized as the best framework to investigate laboratory errors. The three different phases of the TTP - respectively, the pre-analytical, analytical and post-analytical phases - are described in detail and the nature and frequency of errors in each phase explained. For each phase, possible improvements are described and the role of EQA is suggested. Today, EQA principally focuses on the assessment and improvement of the analytical phase. Proposals are made to improve the role of EQA for assessing and improving pre- and post-analytical error as well, by using specific sample material and by automating the reporting of data and laboratory reports to the EQA participants. The principle of the comparison of results of a laboratory with those obtained by the other laboratories is traditionally based on the calculation of "z-scores". An indepth study comparing different techniques has been made, shedding new light on the shortcomings and strong points of the different approaches. We concluded that robust techniques may exhibit weak performance for smaller sample size, while techniques that eliminate outliers before calculating zscores should be recommended. The second Chapter discusses the role of EQA as a tool to assess harmonization between methods. The role of EQA is described, together with the pitfalls and current shortcomings for assessing harmonization. A major problem in assessing standardization between methods is the possible presence of matrix effects in control samples, in which a method-specific bias may appear. Several explanations for matrix effects are mentioned and statistical techniques are described that assist EQA organizers to split up the data in homogeneous peer groups using multivariate statistics. The chapter also reviews several techniques to be used in method comparison studies, and the preference for the use of orthogonal regression is expressed. In addition, an example is given of a method-comparison study for Estradiol and Progesterone, with a novel technique of assessing standardization between various methods, in the presence of matrix effects for a small number of samples. The study also reveals that standardization between various methods is not attained, and that the striving for standardization with standards of higher order may not be satisfactory. Chapter 3 introduces different evaluation techniques that combine information from different samples or parameters: Variance and bias index scores, Mean ranking scores, counts of z- and u-scores, and a long-term analytical Coefficient of Variation. Also, a new and original method is introduced that uses 3 steps to identify outliers in a first step, to find laboratories with exceeding variability in a second, and to identify laboratories with high bias in a third step. Each of the techniques are evaluated and discussed by means of a data set in which accidental outliers, high variability and high bias were induced. In addition, the comparison between the different evaluation methods reveals that distinguishing between variability and bias is a tedious task, and that some long-term analysis methods lack robustness against outliers. Also, it is proven that evaluation techniques summarizing results of different parameters may hide useful information. In addition, the 3-step method is proposed as a method for discerning between errors produced in the pre- or post-analytical phase, and errors that arise from the analytical phase. Chapter 4 applies the 3-step method to data obtained from the Belgian EQA. Data sets from alcohol, flow cytometry, lithium and semen analysis surveys are examined. The method is extended for applicability to heteroscedastic, i.e. unequal residual variability, regression models and demonstrates that it is able to be used in a wide range of surveys. For each of the surveys under consideration, a follow-up is made of the occurrence of accidental mistakes, and the evolution of within-laboratory variability and bias for selected methods. It highlights several conclusions that show a striking similarity for various EQA surveys: an improvement of laboratory performance has been attained over time. The major improvement was a reduction of accidental mistakes. The analytical performance of selected methods, however, did not show an improvement over time. In Chapter 5, some graphical representations of EQA data are explored and a graphical representation of the 3-step method is described. The histogram, normal quantile plot and box plot are described in detail and suggested for providing a quick visual overview of EQA data. Other graphical representations that respond to specific questions are given and discussed as well, like Shewhart charts, Cusum charts and graphical representations to combine variability and bias in one graph. In addition, the 3-step method is graphically explored by means of three distinct graphs. The chapter finishes by suggesting the use of interactive graphs for improving feedback from the EQA organizers to the EQA participants by means of Scalable Vector Graphics. The latter is illustrated with web-accessible examples of long-term evaluation of z-scores and the results of the 3-step method for the data obtained in the Belgian EQA for alcohol determination in blood. In brief, this work describes in a critical and constructive way current statistical methods used in EQA and proposes novel statistical and graphical techniques to help alleviating the future needs of External Quality Assessment programmes.

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