Statistical Quality Control and Routine Data Processing for ...

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Jul 13, 1973 - of data processing. The relative merits of these approaches are discussed. ... A partial listing includes references ...... These programs are still.
CUN. CHEM. 20/10,

1255-1270(1974)

Statistical Quality Control and Routine Data Processing for Radioimmunoassays and Immunoradiometric Assays David Rodbard Numerous methods are available for the graphical display of radioimmunoassay dose-response curves, for curve-fitting and dose interpolation, for statistical quality control, and for automation and computerization of data processing. The relative merits of these approaches are discussed. Minimal requirements for radioimmunoassay

data-processing systems are presented. The features of an “ideal” system are discussed. Additional Keyphrases: statistics dose-response

curves

#{149} data

control

#{149} quality processing

#{149}

The literature on radioimmunoassay (RIA)’ statistics and data processing is large and growing rapidly. A partial listing includes references J_342 Numerous computational systems have become available; each may be modified indefinitely (e.g., use of different computers, languages, addition of input-output options and graphical displays, etc.). However, certain basic

principles

and

requirements

have

now

been

es-

tablished.

Graphical Methods for Portrayal of RIA Dose-Response Curves The

relative

merits

of several

This method is particularly well suited for use with small desk-top calculators, especially if they are equipped with log and antilog functions. This method of linearization can break down, however, under any of three sets of circumstances: (1) marked disparity of concentrations of labeled antigen and antibody [cf. chapter VII of (12)], (2) severely heterogeneous RIA systems, such as some of the “heterologous” assays, or Nonstandard abbreviations used: RIA, radioimmunoassay, IRMA, immunoradiometric assay. Glossary: ajl, constants of eq. 1, intercept, slope a, response for zero dose (also designated B0) b, exponent of eq 2; b = -slope of logit(y) vs log(x) = -$ c, midpoint of dose-response curve; midrange, ED50, ID50 d, response for “infinite” dose (also designated N, “nonspecific”) ao, a1, a2, weighting coefficients for equation (All) B/F, bound-to-free ratio for labeled ligand (corrected for nonspecific counts) BPF, bound-to-total ratio for ligand (corrected for nonspecific counts)

different

systems have been discussed in detail 4, 5, 8, 9, 12). Use of a log transformation

coordinate previously

(1,

of the dose axis is generally advisable. If the RIA analysis is “computerized,” then the dose-response curve can easily be viewed in any or all coordinate systems, taking advantage of the special features of each (5, 8,

(BIT)o, bound-to-total ratio (BIT) for zero dose B/Bo, counts bound for arbitrary dose, relative to that for zero dose (both numerator and denominator corrected for nonspecific counts), B/B0 = (BIT)/(Bfr)0 %B/Bo B/Bo expressed as a percentage, 100.B/Bo. df, degrees of freedom CV, coefficient of variation, expressed as a decimal fraction or as a percentage (%CV) CL, confidence limits logit(y), log5(y/(1 y)) 0 1. If these “impossible” values are simply excluded, one biases the results; a random distribution around 100% would give a mean of less than 100% if all values above 100% are excluded. By use of the response variable for statistical significance tests, we exploit the symmetrical and nearly gaussian nature of the distribution of errors in y as opposed to x. Further, we eliminate any consideration of the uncertainty in estimation of the calibration curve. However, when samples are analyzed at two or more dilutions, or when it is necessary to combine results from several assays, then this approach becomes problematical (though it may be modified, e.g., by using an ANOVA with the “raw counts”) and analysis in terms of x (or some transform thereof) becomes preferable. When multiple treatment groups are present, one should use ANOVA, the multiple ttest, or multiple-range test. Many workers use dozens of conventional t-tests, without realizing that the probability levels of “significance” may be altered drastically when multiple comparisons are made. -

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