scholarly journals Automated Selection of Statistical Quality-Control Procedures to Assure Meeting Clinical or Analytical Quality Requirements

1997 ◽  
Vol 43 (2) ◽  
pp. 400-403 ◽  
Author(s):  
James O Westgard ◽  
Bernard Stein
Keyword(s):  
Quality Control ◽  
Statistical Quality Control ◽  
Analytical Quality ◽  
Quality Requirements ◽  
Statistical Quality ◽  
Control Procedures ◽  
Selection Of

Standard Operating Procedures (SOPs) for Research in Weed Science

1995 ◽  
Vol 9 (2) ◽  
pp. 397-401 ◽  
Author(s):  
William W. Donald ◽  
Paul H. Schwartz
Keyword(s):  
Quality Control ◽  
Research Program ◽  
Critical Review ◽  
Field Research ◽  
Standard Operating Procedures ◽  
Data Accuracy ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Standard Operating ◽  
Control Procedures

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.

Comparison of traditional statistical quality control using commercially available control materials and two patient-based quality control procedures for the ADVIA 120 Hematology System

2018 ◽  
Vol 47 (3) ◽  
pp. 368-376
Author(s):  
Lourdes C. Vanyo ◽  
Kathleen P. Freeman ◽  
Antonio Meléndez-Lazo ◽  
Mariana Teles ◽  
Rafaela Cuenca ◽  
...  
Keyword(s):  
Quality Control ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Control Procedures ◽  
Advia 120

Reference-mean-centered statistical quality control

2020 ◽  
Vol 58 (9) ◽  
pp. 1517-1523
Author(s):  
Martín Yago ◽  
Carolina Pla
Keyword(s):  
Quality Control ◽  
Measurement Procedure ◽  
Statistical Quality Control ◽  
Measuring Instruments ◽  
Control Procedure ◽  
Control Material ◽  
Statistical Quality ◽  
Maximum Benefit ◽  
Control Procedures

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.

Selecting multi-rule quality control procedures based on patient risk

2017 ◽  
Vol 55 (11) ◽  
Author(s):  
Hassan Bayat
Keyword(s):  
Quality Control ◽  
Statistical Model ◽  
Error Rate ◽  
Critical Size ◽  
Traditional Approach ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Close Relationship ◽  
Control Procedures ◽  
The Relationship

AbstractBackground:Traditionally, statistical quality control (SQC) planning is aimed at preventing the error rate from exceeding a pre-defined acceptable rate (Westgard JO. Basic QC Practices, 4th ed. Westgard QC, 2016). A pivotal characteristic for planning a QC procedure with the traditional approach is the probability of rejecting an analytical run that contains critical size errors (PMethods:We used a statistical model to investigate the relationship between PResults:There is a close relationship between PConclusions:Multi-rule SQC procedures can be used for controlling intermediate and low sigma capability method to attain a low Max E(N

Statistical Quality Control of HIV-1 ELISA Test Performance

1994 ◽  
Vol 89 (428) ◽  
pp. 1200-1208 ◽  
Author(s):  
R. C. Gentleman ◽  
M. S. Hamada ◽  
D. E. Matthews ◽  
A. R. Wilson
Keyword(s):  
Quality Control ◽  
Test Performance ◽  
Elisa Test ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Hiv 1
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