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Objective: This simulation study was undertaken to assess the statistical performance of six commonly used rejection criteria for bias detection., Methods: The false rejection rate (i.e. rejection in the absence of simulated bias) and the probability of bias detection were assessed for the following: difference in measurements for individual sample pair, the mean of the paired differences, t-statistics (paired t-test), slope < 0.9 or > 1.1, intercept > 50% of the lower limit of measurement range, and coefficient of determination (R 2 ) > 0.95. The linear regressions evaluated were ordinary least squares, weighted least squares and Passing-Bablok regressions. A bias detection rate of < 50% and false rejection rates of >10% are considered unacceptable for the purpose of this study., Results: Rejection criteria based on regression slope, intercept and paired difference (10%) for individual samples have high false rejection rates and/ or low probability of bias detection. T-statistics (α = 0.05) performed best in low range ratio (lowest-to-highest concentration in measurement range) and low imprecision scenarios. Mean difference (10%) performed better in all other range ratio and imprecision scenarios. Combining mean difference and paired-t test improves the power of bias detection but carries higher false rejection rates., Conclusions: This study provided objective evidence on commonly used rejection criteria to guide laboratory on the experimental design and statistical assessment for bias detection during method evaluation or reagent lot verification., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.)

Background: Between-subject biological variation (CV g ) is an important parameter in several aspects of laboratory practice, including setting of analytical performance specification, delta checks and calculation of index of individuality. Using simulations, we compare the performance of two indirect (data mining) approaches for deriving CV g ., Methods: The expected mean squares (EMS) method was compared against that proposed by Harris and Fraser. Using numerical simulations, d the percentage difference in the mean between the non-pathological and pathological populations, CV i the within-subject coefficient of variation of the non-pathological distribution, f the fraction of pathological values, and e the relative increase in CV i of the pathological distribution were varied for a total of 320 conditions to examine the impact on the relative fractional of error of the recovered CV g compared to the true value., Results: Comparing the two methods, the EMS and Harris and Fraser's approaches yielded similar performance of 158 conditions and 157 conditions within ± 0.20 fractional error of the true underlying CV g , for the normal and lognormal distributions, respectively. It is observed that both EMS and Harris and Fraser's method performed better using the calculated CV i rather than the actual ('presumptive') CV i . The number of conditions within 0.20 fractional error of the true underlying CV g did not differ significantly between the normal and lognormal distributions. The estimation of CV g improved with decreasing values of f, d and CV i CV g ., Discussions: The two statistical approaches included in this study showed reliable performance under the simulation conditions examined., (Copyright © 2022 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.)

Background: Indirect reference intervals and biological variation studies heavily rely on statistical methods to separate pathological and non-pathological subpopulations within the same dataset. In recognition of this, we compare the performance of eight univariate statistical methods for identification and exclusion of values originating from pathological subpopulations., Methods: The eight approaches examined were: Tukey's rule with and without Box-Cox transformation; median absolute deviation; double median absolute deviation; Gaussian mixture models; van der Loo (Vdl) methods 1 and 2; and the Kosmic approach. Using four scenarios including lognormal distributions and varying the conditions through the number of pathological populations, central location, spread and proportion for a total of 256 simulated mixed populations. A performance criterion of ± 0.05 fractional error from the true underlying lower and upper reference interval was chosen., Results: Overall, the Kosmic method was a standout with the highest number of scenarios lying within the acceptable error, followed by Vdl method 1 and Tukey's rule. Kosmic and Vdl method 1 appears to discriminate better the non-pathological reference population in the case of log-normal distributed data. When the proportion and spread of pathological subpopulations is high, the performance of statistical exclusion deteriorated considerably., Discussions: It is important that laboratories use a priori defined clinical criteria to minimise the proportion of pathological subpopulation in a dataset prior to analysis. The curated dataset should then be carefully examined so that the appropriate statistical method can be applied., (Copyright © 2022 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.)

Introduction: Internal quality control (IQC) is traditionally interpreted against predefined control limits using multi-rules or 'Westgard rules'. These include the commonly used 1:3s and 2:2s rules. Either individually or in combination, these rules have limited sensitivity for detection of systematic errors. In this proof-of-concept study, we directly compare the performance of three moving average algorithms with Westgard rules for detection of systematic error., Methods: In this simulation study, 'error-free' IQC data (control case) was generated. Westgard rules (1:3s and 2:2s) and three moving average algorithms (simple moving average (SMA), weighted moving average (WMA), exponentially weighted moving average (EWMA); all using ±3SD as control limits) were applied to examine the false positive rates. Following this, systematic errors were introduced to the baseline IQC data to evaluate the probability of error detection and average number of episodes for error detection (ANEed)., Results: From the power function graphs, in comparison to Westgard rules, all three moving average algorithms showed better probability of error detection. Additionally, they also had lower ANEed compared to Westgard rules. False positive rates were comparable between the moving average algorithms and Westgard rules (all <0.5%). The performance of the SMA algorithm was comparable to the weighted algorithms forms (i.e. WMA and EWMA)., Conclusion: Application of an SMA algorithm on IQC data improves systematic error detection compared to Westgard rules. Application of SMA algorithms can simplify laboratories IQC strategy., (Copyright © 2021 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.)

Objectives: Several guidelines for the evaluation of laboratory tests for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection have recommended establishing an a priori definition of minimum clinical performance specifications before test selection and method evaluation., Methods: Using positive (PPV) and negative predictive values (NPV), we constructed a spreadsheet tool for determining the minimum clinical specificity (conditional on NPV or PPV, sensitivity and prevalence) and minimum clinical sensitivity (conditional on NPV or PPV, specificity and prevalence) of tests., Results: At a prevalence of 1%, there are no minimum sensitivity requirements to achieve a desired NPV of 60%-95% for a given clinical specificity above 20%. It is not possible to achieve 60-95% PPV even with 100% clinical sensitivity, except when the clinical specificity is near 100%. The opposite trend is seen in high prevalence settings (60%), where a relatively low minimum clinical sensitivity is required to achieve a desired PPV for a given clinical specificity, and a higher minimum clinical specificity is required to achieve a desired NPV for a given clinical sensitivity., Discussion: The selection of laboratory tests and the testing strategy for SARS-CoV-2 involves delicate trade-offs between NPV and PPV based on prevalence and clinical sensitivity and clinical specificity. Practitioners and health authorities should carefully consider the clinical scenarios under which the test result will be used and select the most appropriate testing strategy that fulfils the a priori defined clinical performance specification., (Copyright © 2020 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.)

Objectives: The performance of delta check rules has been considered to be dependent on the biological variation characteristics of the analyte of interest. The assumed relationships have not been formally studied. The mathematical relationship between biological variation and delta check rules is explored in this study., Design and Methods: From the mathematical model for absolute difference delta check, the threshold for specificity and sensitivity are observed to be normalized differently. For specificity, the threshold is normalized by the within-subject biological variation (expressed as a coefficient of variation, CV i ), whereas for sensitivity the threshold is normalized by the between-subject biological variation (expressed as a coefficient of variation, CV g ). This highlights the different roles the two biological variations play in affecting the absolute difference distribution for correct and switched patient samples. Analogous to absolute difference delta checks, for relative difference delta checks, the expressions for specificity and sensitivity are scaled by CV i and CV g , respectively. However, the expressions are independent of μ g (the average of the population)., Results: A comparison between the mathematical model and empirical/ historical laboratory data obtained from patients was conducted for both absolute and relative difference delta checks. In general it was found that the specificity obtained from the historical laboratory data was less than the model predicted values, while on the other hand, good correspondence was obtained between the experimental sensitivity and predicted sensitivity., Conclusions: The difference in within-subject biological variation in different patients may contribute to the observed discrepancy in predicted and empirical delta check performance., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.)

Introduction: The clinical catchment area for the Metabolic service at the Women's and Children's Hospital in Adelaide, South Australia, covers nearly 2.5millionkm 2 . Care of children with metabolic disorders in these remote areas is assisted from Adelaide, and at times, using plasma ammonia results from laboratories up to 3000km away. There are seven different platforms measuring plasma ammonia within this vast clinical catchment area. Hence, a correlation study was conducted to examine the relationship between plasma ammonia results from the seven different platforms in use throughout central Australia., Design & Method: Multiple aliquots of plasma from remainder EDTA samples for haematological investigations were frozen. Samples were then dispatched on dry ice to the laboratories being correlated. At an agreed date and time correlation samples were thawed and plasma ammonia measured., Results: Passing-Bablok regression analysis showed slopes ranging from 1.00 to 1.10 and y-intercepts ranging from -10μmol/L to 1μmol/L., Conclusions: Despite the absence of a reference method or reference material and troublesome pre-analytical effects in ammonia measurement, plasma ammonia results from the different platforms in general compare well. The study also demonstrates that samples for ammonia measurement can be transported over great distances and still correlate well. Furthermore, a common reference interval for plasma ammonia may be a possibility., (Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.)