Proficiency Testing in Microbiology: Statistics, performance criteria and the use of proficiency data
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1 Proficiency Testing in Microbiology: Statistics, performance criteria and the use of proficiency data
2 What is Proficiency Testing? Part of quality assurance Demonstration of competence Scheduled interlaboratory testing
3 Proficiency testing: Assigned value Assigned value: Known or consensus NLA-SA Microbiology PT schemes: Consensus value Influence of outliers Outlier: A member of a set of values which is inconsistent with other members of the set extreme high or low Removal of outliers or robust statistical methods NLA-SA Microbiology PT schemes: Robust statistical methods
4 Classical vs robust statistics Classical statistics Mean Standard deviation Robust statistics Robust mean or Median Robust standard deviation or Normalised inter-quartile range (NIQR)
5 Robust mean & Standard deviation Derived by iterative calculation (or repetition) Values of mean and standard deviation updated several times using modified data until the process converges No change from one repetition to the next in the third significant figures Example
6 Median & NIQR: Median explained Median: The number separating the upper half of a data set from the lower half Data arranged from the lowest to the highest value and the middle value determined Example:
7 Median & NIQR: Median explained (cont.) If an even number of data points: Average of two middle items of data Example: = 15
8 Median & NIQR: NIQR explained NIQR: The difference between the 3 rd quartile (Q3) and 1 st quartile (Q1) of the participant laboratories results. First quartile (Q1) = First 25% of results when ranked in order Third quartile (Q3) = First 75% of results when ranked in order NIQR = x (Q3-Q1) (Assumption: Normal distribution)
9 Performance criteria Robust statistics used to calculate performance criteria NLA-SA Microbiology PT schemes: z scores z score: A normalised value which gives a score to each result, relative to the other results in the data set Describes closeness of laboratory s result to consensus value Close to zero: Result agrees well with rest
10 Calculation of z scores Food Microbiology PT scheme: z score = (result obtained by participant robust mean) Robust standard deviation Water Microbiology PT scheme: z score = (result obtained by participant median) NIQR
11 Between & within z scores Duplicate results submitted, denoted A and B Between laboratory z score Demonstrate bias in results: Caused by equipment or operator 1) Calculate standardized sum (S) for each participant: S = (A + B) 2 2) Calculate median & NIQR of all S s, i.e. median(s) and NIQR(S) 3) ZBW = [standardised sum of participant results (S) median(s)] NIQR(S)
12 Between & within z scores (cont.) Within laboratory z score Reflect laboratory s ability to reproduce exactly the same result 1) Calculate standardized difference (D) for each participant: D = (A - B) 2 2) Calculate median & NIQR of all D s, i.e. median(d) and NIQR(D) 3) ZWI = [standardised difference of participant (D) median(d)] NIQR(D)
13 Interpretation of z scores z score close to zero: lab s result agrees well with consensus value Positive z score: lab s result > consensus value Negative z score: lab s result < consensus value
14 Interpretation of z scores (cont.) Conventionally interpreted as follows (ISO 13528): z 2 Satisfactory 2 < z < 3 Questionable z 3 Unsatisfactory Investigate possible causes to identify emerging or recurrent problems Action signal indicating a need for corrective action
15 How to effectively use PT results Set own internal acceptance criteria Read the PT report & review your performance: How close to zero is the lab s z score? Is the lab s result higher or lower than the consensus? Is the result acceptable according to the internal acceptance criteria? Trend your performance: Excel spreadsheet or graph Give feedback
16 How to effectively use PT results (cont.) Do follow up investigations: Check: reported result = result obtained correct method used & instructions followed calibrated equipment used training of staff Implement corrective action Verify: perform test on same sample or another unknown sample
17 Conclusion Better understanding: Microbiology Proficiency Testing Schemes Interpretation of PT results Powerful tool: Identify problems in testing Improve the performance of the laboratory Thank you
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