{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:54:01Z","timestamp":1782842041489,"version":"3.54.5"},"reference-count":41,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIRx Med"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The existence of the variable component of the systematic error (VCSE) was known from the beginning. Still, it is a kind of taboo: it does not have a definition in the International Vocabulary of Metrology and is not present in equations, as it is considered transformed over time into random error.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This theoretical study aims to reevaluate the role and significance of the VCSE in quality control (QC).<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Assuming three quintessential principles\u2014(1) a parameter must be determined under the same conditions under which it is used, (2) a calibration cannot correct smaller biases than the calibration error, and (3) a constant cannot correct a variable\u2014it was deduced that the source of the VCSE is bias drift caused by reagent instability and the shifts caused by human interventions. Both phenomena are mentioned in the literature. The two causes were confirmed by two series of computer simulations using 1000 normally distributed values with an SD of 1 to simulate random error and appropriately chosen bias values to simulate (1) drifts with different slopes and (2) variable shifts. Real-life examples from day-to-day QC, using Roche reagents on Cobas 6000 and Cobas PRO analyzers, confirmed the computer simulations.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \u201cThe bias\u201d is a definitional uncertainty because bias is time-variable. The causes of the cyclic variations are reagent instability and human intervention, confirmed by computer simulation and real-life QC data. Making a clear distinction between bias measured under repeatability and reproducibility within laboratory conditions, as in the case of SDs, and also separating constant and variable subcomponents of the systematic error, 2 sets of error parameters are obtained, each set being consistent with the measurement conditions. The link between them is the time-variable VCSE function. More properties of the VCSE(t) impose a distinction from random error component: predictability and corrigibility in the short term and non-Gaussian distribution. Its transformation into random phenomena is a myth based on confusion between random and variable error components. The accurate determination of the VCSE(t) function is possible, but it has an excessively high cost-effectiveness ratio. Because it is hidden in the bias measured in repeatability and in the SD in reproducibility within laboratory conditions, it helps us to avoid the redundant use in total measurement error and MU equations. Several false assumptions behind the Westgard rules were uncovered.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The new error model aims to serve as the foundation of a new QC system. Internal QC decisions are only consistent with graphs designed using SD measured in repeatability conditions; therefore, they are not consistent with the actual Westgard rules. Alarms should be avoided in cases of incorrigible biases. Immediately after calibration, constant biases, gradually increasing biases, and unexpected shifts in bias represent distinct situations, each requiring a unique strategy.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/49657","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T11:52:09Z","timestamp":1772538729000},"page":"e49657-e49657","source":"Crossref","is-referenced-by-count":1,"title":["Investigating the Variable Component of the Systematic Error, a Neglected Error Parameter: Theoretical Reevaluation Study"],"prefix":"10.2196","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5270-7353","authenticated-orcid":false,"given":"Atilla Barna","family":"Vandra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"issue":"3","key":"R1","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1093\/clinchem\/27.3.493","article-title":"A multi-rule Shewhart chart for quality control in clinical chemistry","volume":"27","author":"Westgard","journal-title":"Clin Chem"},{"issue":"2","key":"R2","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1080\/0025570X.2006.11953386","article-title":"The evolution of the normal distribution","volume":"79","author":"Stahl","journal-title":"Math Mag"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1016\/j.plabm.2020.e00199","article-title":"Biological variation: understanding why it is so important?","volume":"23","author":"Badrick","journal-title":"Pract Lab Med"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.21037\/jlpm-2019-qc-03","article-title":"Past, present, and future of laboratory quality control: patient- based real-time quality control or when getting more quality at less cost is not wishful thinking","volume":"5","author":"Katayev","journal-title":"J Lab Precis Med"},{"issue":"2","key":"R5","doi-asserted-by":"publisher","first-page":"194","DOI":"10.4103\/JLP.JLP_102_17","article-title":"Sigma metrics as a tool for evaluating the performance of internal quality control in a clinical chemistry laboratory","volume":"10","author":"Kumar","journal-title":"J Lab Physicians"},{"key":"R6","unstructured":"[VIM3] 2.19 random measurement error. 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