A Bayesian approach to the evaluation of comparisons of individually value-assigned reference materials

Toman, Blaza; Duewer, David L.; Aragon, Hugo Gasca; Guenther, Franklin R.; Rhoderick, George C.

doi:10.1007/s00216-012-5847-4

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…NIST provides conceptual and computational tools to facilitate transfer of RMPs and material certification technologies to secondary standards producers and users who wish to develop their own higher-order materials. These include ways of evaluating chemical purity (21,22,(46)(47)(48), estimating measurement uncertainty (40,(49)(50)(51), evaluating bias (52,53), designing and analyzing calibration experiments (54), transferring metrological traceability with minimal increase in measurement uncertainty (55,56), establishing traceability of binary SRM mixtures (57), and demonstrating CRM comparability (58,59).…”

Section: Technology Transfermentioning

confidence: 99%

NIST Reference Materials: Utility and Future

Choquette

Duewer

Sharpless

2020

Annual Rev. Anal. Chem.

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The National Institute of Standards and Technology (NIST), formerly the National Bureau of Standards, was established by the US Congress in 1901 and charged with establishing a measurement foundation to facilitate US and international commerce. This broad language provides NIST with the ability to establish and implement its programs in response to changes in national needs and priorities. This review traces some of the changes in NIST's reference material programs over time and presents the NIST Material Measurement Laboratory's current approach to promoting accuracy and metrological traceability of chemical measurements and validation of chemical measurement processes.

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Section: Technology Transfermentioning

confidence: 99%

NIST Reference Materials: Utility and Future

Choquette

Duewer

Sharpless

2020

Annual Rev. Anal. Chem.

Self Cite

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“…(E.g. [1, annex H.3) and [2][3][4][5].) A large subclass of regression problems is defined by measurements that are explained by a linear combination of input variables and measurement errors that are Gaussian.…”

Section: Introductionmentioning

confidence: 99%

“…The use of Bayesian inference for regression tasks is not new in metrology (e.g. [3,5,[9][10][11][12][13][14]). However, little general guidance is given on…”

Section: Introductionmentioning

confidence: 99%

A tutorial on Bayesian Normal linear regression

et al. 2015

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Regression is a common task in metrology and often applied to calibrate instruments, evaluate inter-laboratory comparisons or determine fundamental constants, for example. Yet, a regression model cannot be uniquely formulated as a measurement function, and consequently the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements are not applicable directly. Bayesian inference, however, is well suited to regression tasks, and has the advantage of accounting for additional a priori information, which typically robustifies analyses. Furthermore, it is anticipated that future revisions of the GUM shall also embrace the Bayesian view.Guidance on Bayesian inference for regression tasks is largely lacking in metrology. For linear regression models with Gaussian measurement errors this tutorial gives explicit guidance. Divided into three steps, the tutorial first illustrates how a priori knowledge, which is available from previous experiments, can be translated into prior distributions from a specific class. These prior distributions have the advantage of yielding analytical, closed form results, thus avoiding the need to apply numerical methods such as Markov Chain Monte Carlo. Secondly, formulas for the posterior results are given, explained and illustrated, and software implementations are provided. In the third step, Bayesian tools are used to assess the assumptions behind the suggested approach.These three steps (prior elicitation, posterior calculation, and robustness to prior uncertainty and model adequacy) are critical to Bayesian inference. The general guidance given here for Normal linear regression tasks is accompanied by a simple, but real-world, metrological example. The calibration of a flow device serves as a running example and illustrates the three steps. It is shown that prior knowledge from previous calibrations of the same sonic nozzle enables robust predictions even for extrapolations.

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Comparative evaluation of methods to determine intra‐individual reference ranges in nutrition support team (NST)‐related tests

Hirabayashi

Tsutsumi²,

Sakurai

et al. 2020

Clinical Laboratory Analysis

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Background The intra‐individual reference range is generally narrower than the commonly used reference range. Consequently, close monitoring of changes in the laboratory test results of individuals based on the inter‐individual reference range remains challenging. Methods We examined the determination of individual reference ranges using four indicators of nutritional conditions: transferrin (TRF), albumin (ALB), retinol‐binding protein (RBP), and transthyretin (TTR). The subjects comprised 20 healthy individuals and blood samples were collected and tested five times at 2‐week intervals. We used the measurement results for the four indicators and examined individual reference ranges using four methods, including calculation methods based on the reference change value and Bayesian inference. Results The resulting intra‐individual reference ranges were narrower than the currently used inter‐individual reference range for all measurements using four methods. Furthermore, the intra‐individual coefficient of variation [CV (intra)] was smaller than the inter‐individual coefficient of variation [CV (inter)] for TRF, RBP, and TTR for all 20 subjects. The means CV (intra) for the four indicators were also lower than the corresponding CV (inter). Conclusions The intra‐individual reference range can be used to validate the standard deviation and coefficient of variation for currently used indicators. Moreover, Bayesian methods are speculated to be the most versatile.

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A Bayesian approach to the evaluation of comparisons of individually value-assigned reference materials

Cited by 5 publications

References 9 publications

NIST Reference Materials: Utility and Future

NIST Reference Materials: Utility and Future

A tutorial on Bayesian Normal linear regression

Comparative evaluation of methods to determine intra‐individual reference ranges in nutrition support team (NST)‐related tests

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