Technical Variability in Laboratory Data

Víneis, Paolo; Schulte, Paul A.; Vogt, Robert F.

doi:10.1016/b978-0-08-092566-0.50008-2

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…Accounting for the non-linear dose-response relationship, suitable statistical methods were required to estimate the variance function parameter u (27). Furthermore, the use of an assay design with a geometric progression in calibrator concentrations is unsuitable for building a straight line calibration model (28). Consequently, in the assay current application there are no minimal requirements to check performance characteristics, such as accuracy, precision, analytical sensitivity or interval estimation of unknown sample values.…”

Section: Discussionmentioning

confidence: 99%

Impact of calibration fitting models on the clinical value of chromogranin A

Ferraro

Marano²,

Ciardi

et al. 2009

Clinical Chemistry and Laboratory Medicine

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Background: The clinical relevance of chromogranin A (CgA) concentrations depends on the analytical performance of the assay. The goal of the present study was to define the clinical involvements in CgA calibration models by evaluating the confidence intervals (CIs) for values from patients who were undergoing monitoring for disease. Methods: Thirty calibration curves for the CgA assay wimmunoradiometric assay (IRMA), (CIS-BIO)x were built using linear regression (LR), and four-parameter logistic models were used to estimate CIs for patient concentrations. Results: We reported the inadequacy of the LR curve estimation procedure. We showed: 1) no evidence that the straight calibration line could fit the average responses, 2) non-constant and non-uniform variance of the replicated calibration responses. All tests performed in the analysis of variance and CI calculation for the calibration curve should be invalidated. The four-parameter logistic function yielded results for 16 curves only; this result could be due to the low number and inappropriate concentration of calibrators. This suggests that some aspects of the assay design should be reviewed. However, using the variance function estimated in this model, we could assess the CI for calibration curves and patient samples. Conclusions: We showed that the four-parameter logistic calibration model with estimated variance function should better support clinical interpretation of marker concentration changes in patients serially tested.

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Section: Discussionmentioning

confidence: 99%

Impact of calibration fitting models on the clinical value of chromogranin A

Ferraro

Marano²,

Ciardi

et al. 2009

Clinical Chemistry and Laboratory Medicine

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“…Assessment of reliability encompasses both random laboratory variation and nonrandom (systematic) error. Principles for assessment of marker reliability have been proposed (Vineis et al, 1993;Droz, 1993). Developmental transitional studies should also address aspects of relevance to field applications such as kinetics and stability of the marker.…”

Section: Transitional Studiesmentioning

confidence: 99%

Biomarkers in Cancer Epidemiology

Boffetta¹,

Trichopoulos²,

Adami³

et al. 2018

Oxford Scholarship Online

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Biomarkers have played an important role in cancer epidemiology since the 1980s. Their integration may be used in several ways, including to measure exposures or exposure dose, to assess intermediate effects of risk factors, to understand biological mechanisms of exposures, and to assess cancer subtypes. This chapter describes some of the key methodologic issues and considerations in the use of biological markers in cancer epidemiology. Specifically, issues related to the characterization and variability of biomarkers are considered. In addition, it evaluates the contribution of biomarkers to identifying exposures as carcinogens. Throughout the chapter, methodologic concepts relevant to biomarker research are illustrated with specific examples.

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“…The first step in establishing the analytical validity of a method to test for genetic damage is characterization of the genetic biomarker of interest. Characteristics such as dose-response, biomarker persistence, interindividual and intraindividual variability, methodological variation, correlation with other markers, and correlation with a critical response are crucial [Schulte and Talaska 1995;Vineis et al 1993]. The establishment of a laboratory quality assurance program is essential before any assays of genetic damage are used in research or practice.…”

Section: Validation Of Assays For Evaluation Of Exposure or Genetic D...mentioning

confidence: 99%

Genetics in the workplace: implications for occupational safety and health.

2009

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Technical Variability in Laboratory Data

Cited by 9 publications

References 22 publications

Impact of calibration fitting models on the clinical value of chromogranin A

Impact of calibration fitting models on the clinical value of chromogranin A

Biomarkers in Cancer Epidemiology

Genetics in the workplace: implications for occupational safety and health.

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