Background: The exponentially moving average (EWMA) rule for internal quality control is a well-known type of control rule in industry. Here, the power of the EWMA rule is evaluated to outline the potential of this type of control rule in clinical chemistry.
Methods: Using simulations, the power of the EWMA rule was explicitly compared with that of commonly used rules in clinical chemistry. The type I error levels were standardized to common values to achieve unbiased comparisons.
Results: For small to moderately large errors (systematic errors up to 2–3 standard deviations), the EWMA rule outperforms simple rules (N=1) and multi-rules (N=2–6). For example, for a systematic error of 2s, the EWMA rule equivalent to the 13s rule has a power of 0.30, whereas the 13s rule only displays a power of approximately 0.15. For N=4, comparison was carried out with the 13s/22s/R4s/41s rule. Here the common type I error level is 0.017. At all error levels, the EWMA rule is superior to the multi-rule. For example, given a 1s systematic error, the EWMA rule has a power (0.4) of twice the value of the multi-rule (0.2).
Conclusion: The EWMA rule is an efficient control rule with regard to systematic errors that should be considered for general application in the field of clinical chemistry.
1. Roberts SW. Control chart tests based on geometric moving averages. Technometrics 1959; 1:239–50. Search in Google Scholar
2. Crowder SV. Design of exponentially weighted moving average schemes. J Qual Technol 1989; 21:155–62. Search in Google Scholar
3. Cembrowski GS, Westgard JO, Eggert AA, Toren EC Jr. Trend detection in control data: optimization and interpretation of Trigg's technique for trend analysis. Clin Chem 1975; 21:1396–405. Search in Google Scholar
4. Neubauer AS. The EWMA control chart: properties and comparison with other quality-control procedures by computer simulation. Clin Chem 1997; 43:594–601. Search in Google Scholar
5. Jansen RT, Laeven M, Kardol W. Internal quality control system for non-stationary, non-ergodic analytical processes based upon exponentially weighted estimation of process means and process standard deviation. Clin Chem Lab Med 2002; 40:616–24. Search in Google Scholar
6. Westgard JO. Internal quality control: planning and implementation strategies. Ann Clin Biochem 2003; 40:593–611. Search in Google Scholar
7. Linnet K. Mean and variance rules are more powerful or selective than quality control rules based on individual values. Eur J Clin Chem Clin Biochem 1991; 29:417–24. Search in Google Scholar
8. Winkel P, Zhang NF. Serial correlation of quality control data – on the use of proper control charts. Scand J Clin Lab Invest 2004; 64:195–204. Search in Google Scholar
9. Marquis P. [A new software for quality control at a laboratory]. Ann Biol Clin (Paris) 2000; 58:233–6. Search in Google Scholar
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