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Statistical correction for non-parallelism in a urinary enzyme immunoassay.

Kathleen A O'Connor1, Eleanor Brindle, Jane B Shofer

  • 1Department of Anthropology, Center for Studies in Demography and Ecology, University of Washington, Seattle, Washington 98195, USA. oconnork@u.washington.edu

Journal of Immunoassay & Immunochemistry
|October 6, 2004
PubMed
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A new statistical method using linear mixed-effects modeling corrects non-parallelism in estrone-3-glucuronide (E1G) enzyme immunoassays. This improves the accuracy of hormone measurements, especially for aggregated data.

Area of Science:

  • Biochemistry
  • Endocrinology
  • Statistical Modeling

Background:

  • Enzyme immunoassays (EIAs) for hormone measurement can exhibit non-parallelism between serially diluted samples and calibration curves.
  • Non-parallelism in estrone-3-glucuronide (E1G) EIAs can lead to inaccurate concentration estimations, particularly when analyzing aggregated data.

Purpose of the Study:

  • To develop and validate a statistical method to correct for non-parallelism in E1G enzyme immunoassays.
  • To improve the precision and reliability of urinary E1G measurements.

Main Methods:

  • Linear mixed-effects modeling was employed to analyze the relationship between E1G concentration and urine volume in 40 urine specimens.
  • A statistical correction was derived and validated on independent samples, then applied to data from 30 menstrual cycles.

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  • Assay specificity, detection limit, parallelism, recovery, correlation with serum estradiol, and imprecision were assessed.

    • Main Results:

    • Non-parallelism was observed, with E1G concentration decreasing as urine volume increased (slope = -0.210, p < 0.0001).
    • The statistical correction successfully produced parallelism in 24 independent specimens (slope = -0.043+/-0.010).
    • The correction improved the average coefficient of variation (CV) for E1G concentration across dilutions from 19.5% to 10.3%.

    Conclusions:

    • Linear mixed-effects modeling provides an effective statistical approach to correct for non-parallelism in E1G EIAs.
    • This method enhances the accuracy and consistency of hormone measurements, particularly beneficial for analyzing large datasets.
    • The corrected E1G measurements showed high correlation with serum estradiol levels (r=0.94) across the menstrual cycle.