How to deal with semi-quantitative tests? Application of an ordinal scale model to measurements of urine glucose

Petersen, Per Hyltoft; Christensen, Nina Gade; Sandberg, Sverre; Nordin, Gunnar; Pedersen, Morten; Organizations, Nordic Control

doi:10.3109/00365510902968756

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…Models have been developed to obtain estimators of central location and variability to evaluate different measurement methods ( 41 – 43 ). When titers are involved, the result may be dichotomized, for example by evaluating whether the reported titer would or would not lead to an incorrect conclusion ( 9 ).…”

Section: Qualitative and Semi-quantitative Datamentioning

confidence: 99%

Demystifying EQA statistics and reports

Coucke

Soumali

2017

Biochem Med

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Reports act as an important feedback tool in External Quality Assessment (EQA). Their main role is to score laboratories for their performance in an EQA round. The most common scores that apply to quantitative data are Q- and Z-scores. To calculate these scores, EQA providers need to have an assigned value and standard deviation for the sample. Both assigned values and standard deviations can be derived chemically or statistically. When derived statistically, different anomalies against the normal distribution of the data have to be handled. Various procedures for evaluating laboratories are able to handle these anomalies. Formal tests and graphical representation techniques are discussed and suggestions are given to help choosing between the different evaluations techniques. In order to obtain reliable estimates for calculating performance scores, a satisfactory number of data is needed. There is no general agreement about the minimal number that is needed. A solution for very small numbers is proposed by changing the limits of evaluation. Apart from analyte- and sample-specific laboratory evaluation, supplementary information can be obtained by combining results for different analytes and samples. Various techniques are overviewed. It is shown that combining results leads to supplementary information, not only for quantitative, but also for qualitative and semi-quantitative analytes.

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Section: Qualitative and Semi-quantitative Datamentioning

confidence: 99%

Demystifying EQA statistics and reports

Coucke

Soumali

2017

Biochem Med

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“…The rankit approach has been employed in case studies similar to the present study [7]. In that approach, the observed values are ordered in terms of their relative magnitudes as ranks, r = 1, • • • , n, which can be compared with the Rasch approach to compensating for ordinality as follows:…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…The proposed methodology to modernise the ROC has general applicability. Examples include studies where the instrument-making classifications in support of diagnosis can either be a traditional engineering device (e.g., lateral flow assaying [6] such as pregnancy tests-as studied here-diabetes testing [7] and covid 19 screening tests [8]), or where a human acts as a measurement instrument [4], such as when the abilities of different clinicians to make correct diagnoses are to be assessed. ROC curves are also gaining popularity in modern technological areas such as machine learning, where their use as a metric for assessing machine learning algorithms has become almost indispensable [9].…”

Section: Introductionmentioning

confidence: 99%

Modernising Receiver Operating Characteristic (ROC) Curves

Pendrill

Melin

Stavelin³

et al. 2023

Algorithms

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The justification for making a measurement can be sought in asking what decisions are based on measurement, such as in assessing the compliance of a quality characteristic of an entity in relation to a specification limit, SL. The relative performance of testing devices and classification algorithms used in assessing compliance is often evaluated using the venerable and ever popular receiver operating characteristic (ROC). However, the ROC tool has potentially all the limitations of classic test theory (CTT) such as the non-linearity, effects of ordinality and confounding task difficulty and instrument ability. These limitations, inherent and often unacknowledged when using the ROC tool, are tackled here for the first time with a modernised approach combining measurement system analysis (MSA) and item response theory (IRT), using data from pregnancy testing as an example. The new method of assessing device ability from separate Rasch IRT regressions for each axis of ROC curves is found to perform significantly better, with correlation coefficients with traditional area-under-curve metrics of at least 0.92 which exceeds that of linearised ROC plots, such as Linacre’s, and is recommended to replace other approaches for device assessment. The resulting improved measurement quality of each ROC curve achieved with this original approach should enable more reliable decision-making in conformity assessment in many scenarios, including machine learning, where its use as a metric for assessing classification algorithms has become almost indispensable.

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“…type of virus, bacteria, or mutations whereas an ordinal scale deals with all types of grading, e.g. urine strips for glucose or human chorionic gonadotrophin (hCG) ( 14 , 15 ). Generally, measurements performed on an ordinal scale are measurements that can also be performed on a ratio or interval scale.…”

Section: Measurement Procedures Using the Nominal Or Ordinal Scalementioning

confidence: 99%

Essential aspects of external quality assurance for point-of-care testing

Stavelin¹,

Sandberg

2017

Biochem Med

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External quality assurance (EQA) or proficiency testing for point-of-care (POC) testing is in principle similar to EQA for larger hospital laboratories, but the participants are different. The participants are usually health care personnel with little or no knowledge of laboratory medicine. The implication of this is that the EQA provider has to a) convince the participants that participation in EQA schemes are important, b) be able to circulate materials with reasonable time intervals, c) produce feedback reports that are understandable, and d) offer help and guidance to the participants when needed. It is also important that EQA for POC testing e) address the pre-examination, the examination and the post-examination processes, and f) that schemes for measurement procedures using interval or ordinal scale are offered. The aim of the present paper is to highlight important issues of these essential aspects of EQA for POC testing.

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How to deal with semi-quantitative tests? Application of an ordinal scale model to measurements of urine glucose

Abstract: The present model is a powerful tool for improved characterization of semi-quantitative kits, which makes it possible to evaluate and validate kits and to optimize external quality control.

Cited by 10 publications

References 6 publications

Demystifying EQA statistics and reports

Demystifying EQA statistics and reports

Modernising Receiver Operating Characteristic (ROC) Curves

Essential aspects of external quality assurance for point-of-care testing

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