The fitting, acceptance, and processing of standard curve data in automated immunoassay systems, as exemplified by the Serono SR1 analyzer

Daniels, P.B.

doi:10.1093/clinchem/40.4.513

Cited by 11 publications

(3 citation statements)

References 7 publications

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“…Equation (3) was previously suggested by Daniels 12 as an immunoassay RER model. Equations (2) and (3) are monotone within the data range, whereas Equations (1), (4) and (5) have a single turning point that may or may not occur within the data range.…”

Section: Methodsmentioning

confidence: 99%

Error models for immunoassays

Sadler

2008

Ann Clin Biochem

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A set of rules, based on an objective goodness-of-fit statistic, can be used to automate presentation of the most appropriate function for any particular data-set. Flexibility is easily incorporated into the selection rules and is actually highly desirable to encourage ongoing evaluation with a wider variety of data. A Win32 computer program that performs the variance function estimation and plotting is freely available.

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Section: Methodsmentioning

confidence: 99%

Error models for immunoassays

Sadler

2008

Ann Clin Biochem

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“…The standard 3-parameter function is so named because it was derived 8 from Ekins’ well-known method for constructing repeatability precision profiles (response error is converted into concentration units by standard curve slope 9,10 ). The alternative 3-parameter function was suggested by Daniels 11 for estimating the immunoassay response-error relationship, but it has comparable utility with immunoassay results. In particular, it has superior curvature properties in the detection limit region, whereas the standard 3-parameter function has superior curvature properties at moderate and high concentrations.…”

Section: Variance Functionsmentioning

confidence: 99%

Negative values and variance functions: Implications for statistical analysis

Sadler

2021

Ann Clin Biochem

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When reporting concentrations of substances in biological specimens it has been virtually universal practice to suppress negative results, initially by left-censoring negative results to zero and more recently by left-censoring to values such as limit of blank, limit of detection or even limit of quantification. Negative concentrations are obviously nonsensical and current reporting practices place proper emphasis on assisting the clinician. However, it is easily overlooked that negative concentrations are merely artefacts of data reduction and while adjusting them is sensible clinical practice there are potentially adverse consequences for statistical analysis, in particular for those parametric summaries and analyses which rely on reliable estimates of low-end uncertainty. This article puts a case for the availability of negative results, describes complications with respect to estimating variance functions and discusses practical workarounds.

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“…During an earlier stage of assay development we designed the calibrator curve to cover the fPSA concentration from 0 to 40 ng/mL. A study was carried out to determine which computer curve-fitting program available on our microplate reader (Molecular Device) should be used to construct the calibration curve (13)(14)(15). We took a set of calibrators with known fPSA values and subjected them to the assay.…”

Section: Curve Fittingmentioning

confidence: 99%

Impact of assay parameters on the accuracy of free PSA test: Source and stability of calibrator, calibration curve fitting, and level of total PSA in the serum

Liu

1998

J. Clin. Lab. Anal.

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The measurement of PSA is recommended for men over 50 years of age for screening of prostate cancer. However, proper differentiation of prostate cancer from benign prostate hyperplasia (BPH) relies on an accurate measurement of free PSA (fPSA) and a correct calculation of percent fPSA. Because of the extremely low concentration of fPSA in the serum, any slight deviation from its true value may produce large errors in percent fPSA calculated. Therefore, we undertook a study examining carefully those parameters of the fPSA assay which might affect the fPSA determination. We found that the integrity of the calibrator, the computer curve-fitting program selected, the source of the calibrator, and the total PSA or fPSA + PSA complexes (tPSA) concentration of the specimen all had an impact on the accuracy of the fPSA value assayed. We found that an examination of the slope of the calibration curve was important to reveal whether the calibrator had or had not been denatured during storage. We also found that the 4-parameter cure fitting program was best suited for plotting the fPSA calibration curve. The calibrator we isolated from LNCaP cells was acceptable for our assay because it had an affinity for the assay antibody very similar to that of serum fPSA. We also determined the effect of tPSA concentration on the fPSA determinations and found that within the concentration range of 4-10 ng/mL the impact on the percent fPSA calculated was not significant. We believe that our assay produces accurate fPSA values when all these assay parameters are well controlled.

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The fitting, acceptance, and processing of standard curve data in automated immunoassay systems, as exemplified by the Serono SR1 analyzer

Cited by 11 publications

References 7 publications

Error models for immunoassays

Error models for immunoassays

Negative values and variance functions: Implications for statistical analysis

Impact of assay parameters on the accuracy of free PSA test: Source and stability of calibrator, calibration curve fitting, and level of total PSA in the serum

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