Quantitative laboratory results: Normal or lognormal distribution?
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Klawonn, Hoffmann and Orth.pdf
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Issue Date
2020-06-01
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Show full item recordAbstract
The identification of a suitable distribution model is a prerequisite for the parametric estimation of reference intervals and other statistical laboratory tasks. Classification of normal vs. lognormal distributions from healthy populations is easy, but from mixed populations, containing unknown proportions of abnormal results, it is challenging. We demonstrate that Bowley’s skewness coefficient differentiates between normal and lognormal distributions. This classifier is robust and easy to calculate from the quartiles Q1–Q3 according to the formula (Q1 − 2 · Q2 + Q3)/(Q3 − Q1). We validate our algorithm with a more complex procedure, which optimizes the exponent λ of a power transformation. As a practical application, we show that Bowley’s skewness coefficient is suited selecting the adequate distribution model for the estimation of reference limits according to a recent International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) recommendation, especially if the data is right-skewed.Citation
Journal of Laboratory Medicine 44.3 (2020): 143-150.Affiliation
HZI,Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7,38124 Braunschweig, Germany.Publisher
Walter De Gruyter GmbHJournal
Journal of Laboratory MedicineType
ArticleLanguage
enISSN
25679430EISSN
25679449ae974a485f413a2113503eed53cd6c53
10.1515/labmed-2020-0005
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- Creative Commons