Biomedical subjects
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Estimation of the mean and standard deviation of a distribution using linear combinations of the order statistics of a censored sample is discussed. Existing methods are compared with the optimal estimator of this type and new approximations to the optimal estimator are proposed. Their performances in the presence of outliers are assessed by means of simulation.
One component of method comparison studies is the assessment of relative bias. Most authors use ordinary regression techniques for this purpose. The potential inadequacies in this approach are highlighted and a more generally applicable maximum likelihood technique proposed. The properties of the various methods of estimation are obtained by means of a simulation study, using a variety of different within-assay precision profiles.
This paper emphasizes the need for standardizing the definition and estimation of the minimum detectable analyte concentration in an assay. It reviews the key papers that are most frequently quoted in the literature to illustrate the variety of procedures currently in use.
This survey introduces the subject of internal quality assessment from a historical point of view, presents a unified approach to notation, concepts and definitions, and describes briefly those quality control procedures that are used most commonly in clinical chemistry. It is not the aim of this report to comment on all the individual contributions made in this field, but rather to concentrate on the principles. Particular emphasis is centered around assessment criteria to compare the efficiencies of selected control procedures for monitoring analyte concentrations in biological fluids. The question of whether to compare control schemes by means of average run lengths, run length distribution functions or average cost functions is considered. A rationalized approach based on the comparison of optimal procedures, using average run length, is adopted.
A cumulative sum technique has been specially designed to monitor the error between replicate determinations made on quality control plasma for consecutive batches of assays. This procedure has played a vital role in assessing assay performance. Special consideration has been given to small sample sizes (n = 2 or 3) which is generally the rule rather than the exception in many situations. This technique has been applied to numerous steroid radioimmunoassays and has ensured that both the mean value and the standard error of hormone levels of a quality control pool were under control. Data from routine assays of oestriol and testosterone in plasma from women are presented. Since this technique provides a sensitive measure of monitoring error, it assists the endocrinologist in elucidating statistical inferences which are a manifestation of assay performance.
A modified cumulative sum technique has been applied to radioimmunoassay quality control data. The method is approximately 50% more efficient in detecting systematic changes in the mean and variance of quality control values for plasma samples than more widely used conventional methods. The salient features of the technique have been restricted to changes in the mean quality control value of a plasma pool, but potential applications to changes in variance and as a diagnostic aid to problems in radioimmunoassay have been evaluated. The method is independent of computing facilities and statistical expertise since all computations have been presented in the form of a nomogram and thus can be used by technicians at the bench.
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Estimates of analyte concentrations, obtained by immunoassay, have error distributions which are generally underestimated. Better estimates, which take into account the distribution of the response metameter of the calibration curve and uncertainties associated with the location of the fitted curve, have been obtained by computer simulation using practical immunoassay data. The increased use of curve-fitting software in automated immunoassay equipment has prompted the investigation of reported analyte concentration distributions for three commonly used functions, namely the logit-log, the 4-parameter logistic and the four-parameter Amersham models. The stimulation model is of potential value for a variety of situations.