Le Boedec Kevin
Centre Hospitalier Veterinaire Fregis, Arcueil, France.
Vet Clin Pathol. 2019 Jun;48(2):335-346. doi: 10.1111/vcp.12725. Epub 2019 Jun 22.
According to the ASVCP and other guidelines, samples should comprise at least 120 individuals for reference interval (RI) estimation. Unfortunately, this minimum sample size is difficult to achieve in veterinary medicine. Several statistical methods are described to determine RIs from small sample sizes, but it is unclear which method provides the best accuracy.
This study aimed to compare statistical strategies for estimating RIs and determine which strategy best enhances accuracy when the sample size is between 20 and 120.
Different sample size groups (n = 120, 100, 80, 60, 40, and 20) were randomly selected 50 times from simulated Gaussian, log-normal, and left-skewed populations of 5000 total values. RIs were calculated using seven different statistical strategies comprising robust, parametric, nonparametric, and bootstrap methods, alone or in combination. RI accuracy was compared among these strategies at each sample size. The strategy that was significantly more accurate than others in the largest number of comparisons was considered as the one that best-enhanced RI accuracy.
The strategies that best-enhanced RI accuracy included using the parametric method when the Shapiro-Wilk P > 0.2 and, otherwise, using the nonparametric method to determine the upper and lower RI limits when there were between 60 and 100 reference individuals, and finding the lower RI limit when there were 40 reference individuals. The Box-Cox transformation parametric method best-enhanced RI accuracy of the upper RI limit when there were 40 reference individuals, and the nonparametric method best-enhanced RI accuracy of both RI limits when there were 20 reference individuals.
Using the parametric method when the Shapiro-Wilk P > 0.2, and the nonparametric method in other instances, will likely enhance RI accuracy when there are between 40 and 100 reference individuals. For smaller samples, the nonparametric method might be preferred.
根据美国兽医临床病理学家协会（ASVCP）及其他指南，样本应至少包含120个个体用于参考区间（RI）估计。遗憾的是，在兽医学中难以达到这一最小样本量。已描述了几种从小样本量确定RI的统计方法，但尚不清楚哪种方法能提供最佳准确性。
本研究旨在比较估计RI的统计策略，并确定当样本量在20至120之间时哪种策略能最佳提高准确性。
从总数为5000个值的模拟高斯分布、对数正态分布和左偏态总体中随机抽取不同样本量组（n = 120、100、80、60、40和20），共抽取50次。使用七种不同的统计策略计算RI，这些策略包括稳健、参数、非参数和自助法，单独或组合使用。在每个样本量下比较这些策略的RI准确性。在最多比较次数中显著比其他方法更准确的策略被视为能最佳提高RI准确性的策略。
能最佳提高RI准确性的策略包括：当Shapiro-Wilk P>0.2时使用参数法，否则当有60至100个参考个体时使用非参数法确定RI的上限和下限，当有40个参考个体时确定RI下限。当有40个参考个体时，Box-Cox变换参数法能最佳提高RI上限的准确性，当有20个参考个体时，非参数法能最佳提高RI上下限的准确性。
当Shapiro-Wilk P>0.2时使用参数法，在其他情况下使用非参数法，当有40至100个参考个体时可能会提高RI准确性。对于较小样本，非参数法可能更可取。
