Time series modeling for quality control in clinical chemistry.

1988 ◽  
Vol 34 (7) ◽  
pp. 1396-1406 ◽  
Author(s):  
L C Alwan ◽  
M G Bissell
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Control Charts ◽  
Current Practice ◽  
Clinical Chemistry ◽  
Statistical Quality Control ◽  
New Approach ◽  
Basic Assumptions ◽  
Control Rules ◽  
And Performance

Abstract Autocorrelation of clinical chemistry quality-control (Q/C) measurements causes one of the basic assumptions underlying the use of Levey-Jennings control charts to be violated and performance to be degraded. This is the requirement that the observations be statistically independent. We present a proposal for a new approach to statistical quality control that removes this difficulty. We propose to replace the current single control chart of raw Q/C data with two charts: (a) a common cause chart, representing a Box-Jenkins ARIMA time-series model of any underlying persisting nonrandomness in the process, and (b) a special cause chart of the residuals from the above model, which, being free of such persisting nonrandomness, fulfills the criteria for use of the standard Levey-Jennings plotting format and standard control rules. We provide a comparison of the performance of our proposed approach with that of current practice.

Comparison of quality-control rules used in clinical chemistry laboratories

1993 ◽  
Vol 39 (8) ◽  
pp. 1638-1649 ◽  
Author(s):  
J Bishop ◽  
A B Nix
Keyword(s):  
Quality Control ◽  
Random Error ◽  
Control Charts ◽  
Clinical Chemistry ◽  
Point Of View ◽  
Practical Point ◽  
Control Rules ◽  
Control Schemes ◽  
The Mean ◽  
Fundamental Formula

Abstract Numerous papers have been written to show which combinations of Shewhart-type quality-control charts are optimal for detecting systematic shifts in the mean response of a process, increases in the random error of a process, and linear drift effects in the mean response across the assay batch. One paper by Westgard et al. (Clin Chem 1977;23:1857-67) especially seems to have attracted the attention of users. Here we derive detailed results that enable the characteristics of the various Shewhart-type control schemes, including the multirule scheme (Clin Chem 1981;27:493-501), to be calculated and show that a fundamental formula proposed by Westgard et al. in the earlier paper is in error, although their derived results are not seriously wrong. We also show that, from a practical point of view, a suitably chosen Cusum scheme is near optimal for all the types and combinations of errors discussed, thereby removing the selection problem for the user.

Computerized Preparation of Two-Way Analysis of Variance Control Charts for Clinical Chemistry

1972 ◽  
Vol 18 (3) ◽  
pp. 250-257 ◽  
Author(s):  
J H Riddick ◽  
Roger Flora ◽  
Quentin L Van Meter
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
Computer Program ◽  
Analysis Of Variance ◽  
Control Charts ◽  
Clinical Chemistry ◽  
Control Data ◽  
Laboratory Error ◽  
Quality Control Data

Abstract A system of quality-control data analysis by computer is described, in which two-way analysis of variance is used for partitioning sources of laboratory error into day-to-day, within-day, betweenpools and additivity variation. The partition for additivity is described in detail as to its advantages and applications. In addition, control charts based on two-way analysis of variance computations are prepared each month by computer. This computer program is designed to operate with the IBM 1800 or 1130 computers or any computer with a Fortran IV compiler. Examples are presented of use of the control charts and of tables of analysis of variance.

Monitoring intensive care unit performance using statistical quality control charts

1993 ◽  
Vol 10 (3) ◽  
pp. 155-161 ◽  
Author(s):  
William H. Chamberlin ◽  
Kevin A. Lane ◽  
James N. Kennedy ◽  
Scott D. Bradley ◽  
Charles L. Rice
Keyword(s):  
Intensive Care Unit ◽  
Quality Control ◽  
Intensive Care ◽  
Control Charts ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Quality Control Charts ◽  
Unit Performance

scholarly journals A Fuzzy Control Chart Approach for Attributes and Variables

2018 ◽  
Vol 8 (5) ◽  
pp. 3360-3365 ◽  
Author(s):  
N. Pekin Alakoc ◽  
A. Apaydin
Keyword(s):  
Quality Control ◽  
Fuzzy Control ◽  
Control Chart ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Fuzzy Theory ◽  
New Approach ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Quality Control Chart

The purpose of this study is to present a new approach for fuzzy control charts. The procedure is based on the fundamentals of Shewhart control charts and the fuzzy theory. The proposed approach is developed in such a way that the approach can be applied in a wide variety of processes. The main characteristics of the proposed approach are: The type of the fuzzy control charts are not restricted for variables or attributes, and the approach can be easily modified for different processes and types of fuzzy numbers with the evaluation or judgment of decision maker(s). With the aim of presenting the approach procedure in details, the approach is designed for fuzzy c quality control chart and an example of the chart is explained. Moreover, the performance of the fuzzy c chart is investigated and compared with the Shewhart c chart. The results of simulations show that the proposed approach has better performance and can detect the process shifts efficiently.

scholarly journals On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data

2014 ◽  
Vol 52 (6) ◽  
pp. 3316-3332 ◽  
Author(s):  
Evan B. Brooks ◽  
Randolph H. Wynne ◽  
Valerie A. Thomas ◽  
Christine E. Blinn ◽  
John W. Coulston
Keyword(s):  
Quality Control ◽  
Change Detection ◽  
Control Charts ◽  
Statistical Quality Control ◽  
Landsat Data ◽  
Statistical Quality ◽  
Quality Control Charts

Inspection in Process Control

1998 ◽  
Vol 42 (16) ◽  
pp. 1170-1174
Author(s):  
Somchart Thepvongs ◽  
Brian M. Kleiner
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Signal Detection ◽  
Control Chart ◽  
Control Charts ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Statistical Quality Control ◽  
Total Quality ◽  
Statistical Process

Consistent with the precepts of total quality control and total quality management, there has been a resource shift from incoming and outgoing inspection processes to statistical quality control of processes. Furthermore, process control operators are responsible for their own quality, necessitating the in-process inspection of components. This study treated the statistical process control task of “searching” control charts for out-of-control conditions as an inspection task and applied the Theory of Signal Detection to better understand this behavior and improve performance. Twelve subjects participated in a research study to examine how the portrayal of control chart information affected signal detection theory measures. The type of display did not have a significant effect on the sensitivity and response criterion of subjects. These results are discussed in terms of the applicability of Signal Detection Theory in control chart decision making as well as implications on display design.

scholarly journals Control Charts: An Introduction to Statistical Quality Control.

1948 ◽  
Vol 43 (242) ◽  
pp. 343
Author(s):  
Joseph M. Juran ◽  
Mason E. Wescott ◽  
Edward S. Smith
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Statistical Quality Control ◽  
Statistical Quality
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