Theodorsson Elvar
Department of Clinical Chemistry and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.
Biochem Med (Zagreb). 2015 Oct 15;25(3):311-9. doi: 10.11613/BM.2015.031. eCollection 2015.
Computer-intensive resampling/bootstrap methods are feasible when calculating reference intervals from non-Gaussian or small reference samples. Microsoft Excel® in version 2010 or later includes natural functions, which lend themselves well to this purpose including recommended interpolation procedures for estimating 2.5 and 97.5 percentiles. The purpose of this paper is to introduce the reader to resampling estimation techniques in general and in using Microsoft Excel® 2010 for the purpose of estimating reference intervals in particular. Parametric methods are preferable to resampling methods when the distributions of observations in the reference samples is Gaussian or can transformed to that distribution even when the number of reference samples is less than 120. Resampling methods are appropriate when the distribution of data from the reference samples is non-Gaussian and in case the number of reference individuals and corresponding samples are in the order of 40. At least 500-1000 random samples with replacement should be taken from the results of measurement of the reference samples.
当从非高斯分布或小样本参考样本计算参考区间时，计算机密集型重采样/自助法是可行的。2010版或更高版本的Microsoft Excel®包含一些自然函数，非常适合用于此目的，包括用于估计第2.5和第97.5百分位数的推荐插值程序。本文的目的是向读者介绍一般的重采样估计技术，特别是介绍如何使用Microsoft Excel® 2010来估计参考区间。当参考样本中的观测值分布为高斯分布，或者即使参考样本数量少于120时也可以转换为该分布时，参数方法比重采样方法更可取。当参考样本的数据分布为非高斯分布，且参考个体和相应样本数量约为40时，重采样方法是合适的。应从参考样本的测量结果中至少抽取500 - 1000个有放回的随机样本。
