Reference-mean-centered statistical quality control

2020 ◽  
Vol 58 (9) ◽  
pp. 1517-1523
Author(s):  
Martín Yago ◽  
Carolina Pla

AbstractBackgroundStatistical quality control (SQC) procedures generally use rejection limits centered on the stable mean of the results obtained for a control material by the analyzing instrument. However, for instruments with significant bias, re-centering the limits on a different value could improve the control procedures from the viewpoint of patient safety.MethodsA statistical model was used to assess the effect of shifting the rejection limits of the control procedure relative to the instrument mean on the number of erroneous results reported as a result of an increase in the systematic error of the measurement procedure due to an out-of-control condition. The behaviors of control procedures of type 1ks (k = 2, 2.5, 3) were studied when applied to analytical processes with different capabilities (σ = 3, 4, 6).ResultsFor measuring instruments with bias, shifting the rejection limits in the direction opposite to the bias improves the ability of the quality control procedure to limit the risk posed to patients in a systematic out-of-control condition. The maximum benefit is obtained when the displacement is equal to the bias of the instrument, that is, when the rejection limits are centered on the reference mean of the control material. The strategy is sensitive to error in estimating the bias. Shifting the limits more than the instrument’s bias disproportionately increases the risk to patients. This effect should be considered in SQC planning for systems running the same test on multiple instruments.ConclusionsCentering the control rule on the reference mean is a potentially useful strategy for SQC planning based on risk management for measuring instruments with significant and stable uncorrected bias. Low uncertainty in estimating bias is necessary for this approach not to be counterproductive.

1995 ◽  
Vol 9 (2) ◽  
pp. 397-401 ◽  
Author(s):  
William W. Donald ◽  
Paul H. Schwartz

Standard operating procedures (SOPs) were developed for repetitive field research tasks to help ensure that instructions were complete and to provide consistency and continuity in the senior author's field research program. SOPs are explicit step-by-step instructions for carrying out experimental tasks that are components of experimental plans. SOPs are not the same as protocols for unique, new experimental plans. However, protocols may incorporate sequences of SOPs, if desired. SOPs are most useful for new workers and when research tasks need to be repeated infrequently in time (e.g., once every 6 mo or less per year). SOPs may help researchers enhance data accuracy, precision, and reproducibility as part of their own statistical quality control procedures. The authors' field-tested SOPs are available on diskette for critical review, modification, and use by interested weed scientists.


1976 ◽  
Vol 20 (1) ◽  
pp. 1-5 ◽  
Author(s):  
C. G. Drury

Recent progress in the Statistical Quality Control field has led to the design of Sampling plans which do not assume perfect inspection. Simple methods now exist for analyzing the effect of inspector error on the operating characteristic (OC) curve of a plan and further for re-designing the plan so that a predetermined OC curve is obtained. However, the usual assumption made about human inspection error is that it is constant. Many studies show that Type 1 and Type 2 inspector error change systematically with many variables such as input quality, complexity of item inspected, type of fault, standards, individual differences, etc. This paper develops a methodology for including an explicit human inspector model into the sampling plan design. A particular model integrating visual search and decision making (proposed earlier by the author) is used to demonstrate the feasibility of including explicit human inspector data in the design process. The applications of this model to single and double sampling plans are discussed, together with evidence for the validity of the model under laboratory and field conditions.


1972 ◽  
Vol 18 (9) ◽  
pp. 918-922 ◽  
Author(s):  
Tsann Ming Chu ◽  
Gustavo Reynoso

Abstract A radioimmunoassay of carcinoembryonic antigen in plasma is described and evaluated. The assay can be easily performed and implemented in a clinical laboratory. Assessed by Rodbard’s statistical quality-control procedure, the assay is shown to be highly sensitive, precise, and reproducible.


Author(s):  
Colin G. Drury

Recent progress in the statistical quality control field has led to the design of sampling plans which do not assume perfect inspection. Simple methods now exist for analyzing the effect of inspector error on the operating characteristic (OC) curve of a plan and further for redesigning the plan so that a predetermined OC curve is obtained. However, the usual assumption made about human inspection error is that it is constant. Many studies show that Type 1 and Type 2 inspector errors change systematically with many variables such as input quality, complexity of item inspected, type of fault, standards, individual differences, etc. This paper develops a methodology for including an explicit human inspector model into the sampling plan design. A particular model integrating visual search and decision making (proposed earlier by the author) is used to demonstrate the feasibility of including explicit human inspector data in the design process. The applications of this model to single and double sampling plans are discussed, together with evidence for the validity of the model under laboratory and field conditions.


2018 ◽  
Vol 47 (3) ◽  
pp. 368-376
Author(s):  
Lourdes C. Vanyo ◽  
Kathleen P. Freeman ◽  
Antonio Meléndez-Lazo ◽  
Mariana Teles ◽  
Rafaela Cuenca ◽  
...  

2018 ◽  
Vol 151 (4) ◽  
pp. 364-370 ◽  
Author(s):  
James O Westgard ◽  
Sten A Westgard

AbstractObjectivesTo establish an objective, scientific, evidence-based process for planning statistical quality control (SQC) procedures based on quality required for a test, precision and bias observed for a measurement procedure, probabilities of error detection and false rejection for different control rules and numbers of control measurements, and frequency of QC events (or run size) to minimize patient risk.MethodsA Sigma-Metric Run Size Nomogram and Power Function Graphs have been used to guide the selection of control rules, numbers of control measurements, and frequency of QC events (or patient run size).ResultsA tabular summary is provided by a Sigma-Metric Run Size Matrix, with a graphical summary of Westgard Sigma Rules with Run Sizes.ConclusionMedical laboratories can plan evidence-based SQC practices using simple tools that relate the Sigma-Metric of a testing process to the control rules, number of control measurements, and run size (or frequency of QC events).


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