Plasma Electrolyte Distributions in Humans—Normal or Skewed?

Feldman, Mark; Dickson, Beverly

doi:10.1016/j.amjms.2017.07.012

Cited by 6 publications

(8 citation statements)

References 10 publications

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“…2 Few studies have assessed the frequency distribution of a wide range of routine biochemistry analytes in people. [10][11][12] In these studies, three distribution shapes were described. Most analytes showed either a Gaussian or a log-normal frequency distribution, with approximately as many Gaussian-distributed analytes as lognormal-distributed ones; few analytes showed a left-skewed distribution.…”

Section: Discussionmentioning

confidence: 99%

“…Most analytes showed either a Gaussian or a log-normal frequency distribution, with approximately as many Gaussian-distributed analytes as lognormal-distributed ones; few analytes showed a left-skewed distribution. [10][11][12] Although bimodal distribution has occasionally been reported, the overall distribution shape still could be seen as globally Gaussian, log-normal, or left skewed. 12 Therefore, a statistical strategy that takes into account these three main distribution shapes intuitively seems more appropriate.…”

Section: Discussionmentioning

confidence: 99%

“…[10][11][12] Although bimodal distribution has occasionally been reported, the overall distribution shape still could be seen as globally Gaussian, log-normal, or left skewed. 12 Therefore, a statistical strategy that takes into account these three main distribution shapes intuitively seems more appropriate. This hypothesis was supported by our results as the accuracy of the statistical method associated with the best RI estimation differed depending on the population distribution.…”

Section: Discussionmentioning

confidence: 99%

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Subjective assessment of frequency distribution histograms and consequences on reference interval accuracy for small sample sizes: A computer‐simulated study

Coisnon

Mitchell

Rannou³

et al. 2021

Veterinary Clinical Pathol

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Background: Inaccuracy in estimating reference intervals (RIs) is a problem with small sample sizes.Objectives: This study aimed to identify the most accurate statistical methods to estimate RIs based on sample size and population distribution shape. We also studied the accuracy of sample frequency distribution histograms to retrieve the original population distribution and compared strategies based on the histogram and goodness-of-fit test. Methods:The statistical methods that best enhanced accuracy were determined for various sample sizes (n = 20-60) and population distributions (Gaussian, log-normal, and left-skewed) were determined by repeated-measures ANOVA and posthoc analyses. Frequency distribution histograms were built from 900 samples of five different sizes randomly extracted from six simulated populations. Three reviewers classified the population distributions from visual assessments of a sample histogram, and the classification error rate was calculated. RI accuracy was compared among the strategies based on the histograms and goodness-of-fit tests. Results:The parametric, nonparametric, and robust methods enhanced lower reference limit estimation accuracy for Gaussian, log-normal, and left-skewed distributions, respectively. The parametric, nonparametric bootstrap, and nonparametric methods enhanced the upper limit estimation accuracy for Gaussian, log-normal, and left-skewed distributions, respectively. Regardless of sample size, sample histogram assessments properly classified the original population distribution 71% to 93.9% of the time, depending on the reviewers. In this study, the strategy based on histograms assessed by the statistician was significantly more precise and accurate than the strategy based on the goodness-of-fit test (P < 0.001). Conclusions:A strategy based on histograms might enhance the accuracy of RI estimations. However, relevant inter-reviewer variations in histogram interpretation were detected. Factors affecting inter-reviewer variations should be further explored.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Subjective assessment of frequency distribution histograms and consequences on reference interval accuracy for small sample sizes: A computer‐simulated study

Coisnon

Mitchell

Rannou³

et al. 2021

Veterinary Clinical Pathol

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“…Our study shows that Bowley's quartile skewness is a simple and robust method to classify normality vs. lognormality in mixed populations (Table 1). In the simplest version, the 95% confidence interval for the skewness of normally distributed data may be used to make a safe classification of normally distributed sodium [2] and hemoglobin [17] as well as lognormally distributed WBC [16] and ALAT [14] test results. For K and Crea, the basic algorithm predicted different distributions for women and men, which were due to irregularities in the shapes of the density curves (Figure 3).…”

Section: Discussionmentioning

confidence: 99%

“…Quantitative laboratory results of healthy individuals usually show either a symmetric or a right-skewed histogram [1,2]. The former may be described by a normal and the latter by a lognormal distribution, although real data probably never follow exactly ideal distributions in the form of normal, lognormal or other simple types of distributions.…”

Section: Introductionmentioning

confidence: 99%

Quantitative laboratory results: normal or lognormal distribution?

Klawonn

Hoffmann

Orth

2020

Journal of Laboratory Medicine

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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.

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A property-response perspective on modern toxicity assessment and drug toxicity index (DTI)

Dixit

Singh

2021

In Silico Pharmacol.

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Plasma Electrolyte Distributions in Humans—Normal or Skewed?

Cited by 6 publications

References 10 publications

Subjective assessment of frequency distribution histograms and consequences on reference interval accuracy for small sample sizes: A computer‐simulated study

Subjective assessment of frequency distribution histograms and consequences on reference interval accuracy for small sample sizes: A computer‐simulated study

Quantitative laboratory results: normal or lognormal distribution?

A property-response perspective on modern toxicity assessment and drug toxicity index (DTI)

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