Trueness of measurement

The present discussion is about the constant bias of the measurement procedure (the first term, called constant inaccuracy, in the equation above). This component of overall bias is, in principle, a known detriment to trueness of measurement (defined as average closeness to a reference value). 

Systematic error is an error that, during multiple measurements performed under the same conditions, remains constant. Its value cannot be calculated without knowing the actual value or a value contractually accepted as real. Systematic errors, which should be small, determine the trueness of measurement. This type of error can be a parameter of a single measurement or of an analytical process, in which case it is known as bias [2]. 

Trueness of measurements is defined as closeness of agreement between the average value obtained from a large series of results of measurements and a true value. The difference between the average value (strictly, the mathematical expectation) and the true value is the bias, which is expressed numerically and so is inversely related to the trueness. Trueness in itself is a qualitative term that can be expressed as. 

Precision may be defined as the closeness of agreement between independent results of measurements obtained under stipulated conditions.The degree of precision is usually expressed on the basis of statistical measures of imprecision, such as the SD or CV (CV = SD/x, where x is the measurement concentration), which thus is inversely related to precision. Imprecision of measurements is solely related to the random error of measurements and has. no relation to the trueness, of measurements. 

The ISO recommendation [1993] should be followed and accuracy used only as a qualitative term. In case of quantitative characterization (by means of the bias), a problem may appear which is similar to that of precision, namely that a quality criterion is quantified by a measure that has a reverse attribute regarding the property which have to be characterized. If the basic idea of measures can be accepted, which is that a high quality becomes a high value and vice versa, bias is an unsuited measure of accuracy (and trueness). In this sense, accuracy could be defined by means of a measure proposed in the next paragraph. 

The example given in Table 7.2 is taken from a study to verify the trueness of clinical analyses (Streck [2004]). Recovery rates have been used as the criterion to accept a good agreement between the measured results and the reference values as it is frequently done by analysts. 

The situation becomes more complex when aspects of the trueness of analytical results are included in the assessment. Trueness of information cannot be considered neither by the classical Shannon model nor by Kullback s divergence measure if information. Instead, a model that takes account of three distributions, viz the uniform expectation range, po(x), the distribution of the measured values, p(x), and that of the true value, r(x), as shown in Fig. 9.5, must be applied. 

Measurements are subject to systematic errors as well as the random errors covered in Section 4.3.2. Bias is the difference between the mean value of a large number of test results and an accepted reference value for the test material. The bias is a measure of trueness of the method. It can be expressed in a number of ways, i.e. simply as a difference or as a ratio of the observed value to the accepted value. This latter representation, when expressed as a percentage, is often termed recovery. This represents how much of the analyte of interest has been extracted from the matrix and measured. This is dealt with in Section 4.6.3. 

Accuracy (Trueness and Precision) of Measurement Methods and Results - Part 1. General Principles and Definitions , ISO 5725-1 1994, International Organization for Standardization (ISO), Geneva, Switzerland 1994. 

Bias is a measure of trueness . It tells us how close the mean of a set of measurement results is to an assumed true value. Precision, on the other hand, is a measure of the spread or dispersion of a set of results. Precision applies to a set of replicate measurements and tells us how the individual members of that set are distributed about the calculated mean value, regardless of where this mean value lies with respect to the true value. 

International Organization for Standardization (ISO), Statistical methods for quality control, Vol. 2, 4th Edition, Accuracy (trueness and precision) of measurement methods and results - Part 2 Basic method for the determination of repeatability and reproducibility of a standard measurement method, ISO 1994(E), 5725-2. 

International organization for Standardization, Accuracy (trueness and precision) of measurement methods and results, ISO/DIS 5725-1 to 5725-3, Draft versions 1990/91. 

One or more of these bias components are encountered when analyzing RMs. In general, RMs are divided into certified RMs (CRMs, either pure substances/solu-tions or matrix CRMs) and (noncertified) laboratory RMs (LRMs), also called QC samples [89]. CRMs can address all aspects of bias (method, laboratory, and run bias) they are defined with a statement of uncertainty and traceable to international standards. Therefore, CRMs are considered useful tools to achieve traceability in analytical measurements, to calibrat equipment and methods (in certain cases), to monitor laboratory performance, to validate methods, and to allow comparison of methods [4, 15, 30]. However, the use of CRMs does not necessarely guarantee trueness of the results. The best way to assess bias practically is by replicate analysis of samples with known concentrations such as reference materials (see also Section 8.2.2). The ideal reference material is a matrix CRM, as this is very similar to the samples of interest (the latter is called matrix matching). A correct result obtained with a matrix CRM, however, does not guarantee that the results of unknown samples with other matrix compositions will be correct [4, 89]. 

Modern measurement has tried to get away from the traditional concepts of accuracy or trueness. These concepts are based on the false assumption that there is a single true value that lurks in the measurement system and that in principle can be accessed by a sufficient number of measurements done with sufficient attention to detail. In reality, the measurement defines to a large... 

If a method must be developed from scratch, or if an established method is changed radically from its original published form, then before the method is validated, the main task is simply to get the method to work. This means that the analyst is sure that the method can be used to yield results with acceptable trueness and measurement uncertainty (accuracy). When the analyst is satisfied that the method does work, then the essentials of method validation will also have been done, and now just need to be documented. If there is an aspect of the method that does not meet requirements, then further development will needed. Discovering and documenting that the method now does satisfy all requirements is the culmination of method validation. 

Abstract The concept of total allowable error , investigated by Westgard and co-workers over a quarter of a century for use in laboratory medicine, comprises bias as well as random elements. Yet, to minimize diagnostic misclassifi-cations, it is necessary to have spatio-temporal comparability of results. This requires trueness obtained through metrological traceability based on a calibration hierarchy. Hereby, the result is associated with a final uncertainty of measurement purged of known... 

Key words Metrological traceability Total allowable error Trueness Unbiased result Uncertainty of measurement... 

The necessary anchor for the trueness of a measurement procedure is obtained by strict metrological traceability of result, based on a calibration hierarchy. The... 

Anklam et al. [7] as well as Ahmed [8] recently published a comprehensive overview of different PCR assays that have been published in the literature. The authors tried to include performance data adding to the value of the review articles. The validation of PCR methods and thus the establishment of such performance criteria is still the subject of much debate. H bner et al. [9] suggested an approach for the validation of PCR assays. In general, it is currently the view of most researchers that validation of a PCR assay should not differ essentially from the validation of other analytical methods. Thus, all principles outlined in the ISO standard 17025 General requirements for the competence of testing and calibration laboratories, ISO standard 5725 Accuracy (trueness and precision) of measurement methods and results as well as the principles as laid down by Codex Alimentarius (http //www.co-dexalimentarius.net), are applicable to PCR. 

T raceability is the property of result of measurement or the value of a standard whereby it can be related to stated references, usually national or international standards, through an unbroken chain of comparisons all having stated uncertainty [16]. Traceability is the proof of trueness of a result. Traceability is also a measure to build up trust in measurement results because it includes a complete documentation of calibration certificates (Figs. 7 and 8). 

ISO 5725-5 1998 Accuracy (trueness and precision) of measurement methods and results—Part 5 alternative methods for the determination of the precision of a standard measurement method ISO 5725-6 1994 Accuracy (trueness and precision) of measurement methods and results—Part 6 use in practice of accuracy values. http //www.iso.org/iso/search.htm qt=5725 published= on active tab=standards... 

Meeting stringent performance characteristics for each analyte (in particular test accuracy, including both precision of the measurement and trueness of the measurement), and... 

ISO, 1994, International Standard ISO 5725 "Accuracy (trueness and precision) of measurement method and results", 1994-12. 

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