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

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Tytuł:: Subjective assessment of frequency distribution histograms and consequences on reference interval accuracy for small sample sizes: A computer-simulated study.
Autorzy:: Coisnon C; Laboratoire Vebio, Arcueil, France.
Mitchell MA; Veterinary Clinical Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, USA.
Rannou B; AzurVet-Lab, Saint-Laurent-du-Var, France.
Le Boedec K; Internal Medicine Unit, CHV Fregis, Arcueil, France.
Źródło:: Veterinary clinical pathology [Vet Clin Pathol] 2021 Sep; Vol. 50 (3), pp. 427-441. Date of Electronic Publication: 2021 Sep 02.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Publication: 1998- : Baton Rouge, LA : American Society for Veterinary Clinical Pathology
Original Publication: 1977-<1989> : Columbia, MO : Veterinary Practice Pub. Co.
MeSH Terms:: Computers*
Animals ; Computer Simulation ; Normal Distribution ; Reference Values ; Sample Size
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Contributed Indexing:: Keywords: Gaussian distribution; bootstrap; left-skewed distribution; log-normal distribution; nonparametric method; parametric method; robust method
Entry Date(s):: Date Created: 20210903 Date Completed: 20210930 Latest Revision: 20210930
Update Code:: 20240105
DOI:: 10.1111/vcp.13000
PMID:: 34476826
: Czasopismo naukowe

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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.
(© 2021 American Society for Veterinary Clinical Pathology.)

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