Although the correlation of the ARCHITECT insulin assay with the E-test TOSOH II (IRI) was good across the clinical measurement range, we detected slope deviation. To investigate the cause of the slope deviation, we confirmed that both assays were exactly calibrated against the WHO reference preparation.
In the comparison test, however, we observed slope deviation. Standardization of immunoassays for insulin remains a problem despite the availability of large quantities of human insulin through recombinant DNA technology (2).
cause of the slope deviation, it does suggest the influence of a matrix component and the need for further investigation.
One may then derive the needs for detecting critical sizes of [[alpha].sub.0] and slope deviation from unity ([beta] - 1):
A general, simplified formula for the approximation of the necessary sample size for detection of a difference [DELTA] with regard to slope deviation from unity or intercept deviation from zero (16) is:
With regard to the slope, this value refers to the slope deviation from unity measured in [CV.sub.a] units:
Relating the systematic difference to a slope deviation corresponds to a demand of detecting [beta] = 1.12 (3.35/3), or 0.88.
To get the necessary sample size, we consult Table 1 and look under a range ratio of 2 and a standardized slope deviation of 4 and find the sample size, N = 41.
By squared extrapolation, we obtain the approximate value N = 19 [40 X [(4/5.8).sup.2].] Thus, in this example, the sample size requirement with regard to testing for intercept deviation from zero is less demanding than that of testing for a critical slope deviation.
Ascribing the systematic difference to a slope deviation implies a demand of detecting [beta] = 1.05 or 0.95.