QUANTIFYING THE EFFECT OF SETTING QUALITY CONTROL STANDARD DEVIATIONS GREATER THAN ACTUAL STANDARD DEVIATIONS ON WESTGARD RULES
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1 AACC24 QUANTIFYING THE EFFECT OF SETTING QUALITY CONTROL STANDARD DEVIATIONS GREATER THAN ACTUAL STANDARD DEVIATIONS ON WESTGARD RULES Graham Jones Department of Chemical Pathology, St Vincent s Hospital, Sydney, Australia.
2 Background AACC24 2 Westgard rules are commonly used to detect changes in assay performances. The Power of Error Detection (PED) of rules can be determined from available sources. These sources assume that the SD set in the QC protocol is the actual SD of the measurement system. Sometimes the SD in QC charts is set wider than the actual instrument SD (figure ). Here I investigate the effect of setting QC limits wider than the actual SDs on the Power of Error Detection..
3 +2-2 A Setting QC Limits - illustration QC SD = Actual SD Spread of QC results across range. 5% of results outside +/- 2SD +2-2 B QC SD = 3 x Actual SD Same ASD as graph A. QC results clustered near mean. No results outside +/- 2SD AACC24 3 Figure
4 Hypothesis Setting the SD limits in the Levy-Jennings QC chart different to the actual instrument SD limits will change the performance of the QC protocol. Aim To investigate the utility of published Power Function Charts to determine the Power of Error Detection when the QCSD is larger than the Actual SD of the method. AACC24 4
5 Terminology AACC24 5 ASD - The actual SD of the measurement system QCSD - The SD set in the QC package or drawn on Levy-Jennings chart. PED: Power of Error Detection - the likelihood that a QC rule will trigger for any given assay drift. ED 9 : The error detected with a 9% probability. 3s - result outside 3 SD 2s - result outside 2 SD 2 2s - 2 results outside 2 SD 4 s - 4 consecutive results outside SD n - the number of QC samples run at the same time
6 Methodology Power Function Charts were developed in Microsoft Excel. Random variation was simulated using the random number generator with a normal distribution (n=). Bias was simulated by adding fixed amounts to the randomly generated numbers. Changes in imprecision were modelled by multiplying output from random number generator. Rules were modelled for n=2 and n=4. Changes in bias and precision were modelled. For n= 2, The 2 2s rule was run within the pair of simultaneous QCs For n=4, the 4 s rule was run on data from both materials. AACC24 6
7 Results and Discussion Increasing the QCSD relative to the ASD changes the Power Function Charts for detecting bias (n=2, figure 2; n=4, figure 6) and precision (n=2, figure 3). For changes in bias the PED for various values of QCSD/ASD can be modelled (n=2, figure 4). For changes in bias the ED 9 for various values of QCSD/ASD can be modelled (n=2, fig 5; n=4, fig 6). ED 9 changes with QCSD/ASD at different rates for different rules and combinations of rules. The ED 9 can be calculated for various rules from the data presented (see figures 5 and 6 for data). AACC24 7
8 PED Power Function Chart (bias, n=2) AACC Shift (multiples of SD) QCSD/ASD ED 9 Effect of increasing QCSD relative to ASD on Power of Error Detection (PED) for various changes in bias. Rules: 3s /2 2s /R 4s. n=2. ED 9 is used for later calculations (fig 5) Conclusion: PED for bias falls as QCSD/ASD increases. Figure
9 PED Power Function Charts (precision, n=2) QCSD/ASD Precision (multiples of SD) Effect of increasing QCSD / ASD on the Power of Error Detection (PED) for changes in assay precision. Rules: 3s /2 2s /R 4s. N=2. Note: PED never reaches 9%. Conclusion: PED for precision falls as QCSD/ASD increases. AACC24 9 Figure 3
10 PED Increasing QCSD / ASD: Effect on PED QCSD / ASD Shift (multiples of ASD) Effect of increasing QCSD/ASD on the Power of Error Detection for various shifts in assay bias. Rules: 3s /2 2s /R 4s. n=2. Conclusion: PED falls as QCSD/ASD increases. AACC24 Figure 4
11 Increasing QCSD / ASD: Effect on ED 9 ED QCSD / ASD ED 9 Prediction Equations 3s /2 2s /R 4s ED 9 =2.3 x QCSD/ASD s ED 9 =2. x QCSD/ASD +.6 3s ED 9 =3. x QCSD/ASD +.4 Effect of increasing QCSD/ASD on the Error Detection with 9% probability (ED 9 )for individual rules and combinations of rules. n=2. Conclusion: ED 9 increases linearly with increasing QCSD/ASD. AACC24 Figure 5
12 PED ED AACC B A Power Function Charts (bias, n=4) Shift (multiples of SD) QCSD / ASD QCSD/ASD ED A. Effect of increasing QCSD / ASD on PED. N=4. Rules: 3s /2 2s /R 4s /4 s. n=4. B. ED 9 for various rules. n=4. ED 9 Prediction Equations 3s /2 2s /4 s /R 4s ED 9 =.5 x QCSD/ASD +.8 3s ED 9 = 3. x QCSD/ASD s ED 9 = 2. x QCSD/ASD s ED 9 =. x QCSD/ASD + 2 Conclusion: Data similar for n=4. Figure 6
13 Conclusions Setting the QCSD wider than the ASD in QC protocols affects the performance of Westgard rules. If QCSD does not equal to ASD the published Power Function Charts do not accurately represent the power of error detection. Laboratories wishing to determine the power of error detection using published data must either set QCSD=ASD; apply corrections as outlined in this poster; or develop alternate methods to determine their own error detection power. AACC24 3
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