Røraas Thomas, Støve Bård, Petersen Per Hyltoft, Sandberg Sverre
Norwegian Quality Improvement of Primary Care Laboratories (NOKLUS), Haraldsplass Deaconess Hospital, Bergen, Norway; Department of Global Public Health and Primary Care, University of Bergen, Norway.
Department of Mathematics, University of Bergen, Norway.
Clin Chim Acta. 2017 May;468:166-173. doi: 10.1016/j.cca.2017.02.021. Epub 2017 Feb 28.
Precise estimates of the within-person biological variation, CV, can be essential both for monitoring patients and for setting analytical performance specifications. The confidence interval, CI, may be used to evaluate the reliability of an estimate, as it is a good measure of the uncertainty of the estimated CV. The aim of the present study is to evaluate and establish methods for constructing a CI with the correct coverage probability and non-cover probability when estimating CV.
Data based on 3 models for distributions for the within-person effect were simulated to assess the performance of 3 methods for constructing confidence intervals; the formula based method for the nested ANOVA, the percentile bootstrap and the bootstrap-t methods.
The performance of the evaluated methods for constructing a CI varied, both dependent on the size of the CV and the type of distributions. The bootstrap-t CI have good and stable performance for the models evaluated, while the formula based are more distribution dependent. The percentile bootstrap performs poorly.
CI is an essential part of estimation of the within-person biological variation. Good coverage probability and non-cover probabilities for CI are achievable by using the bootstrap-t combined with CV-ANOVA. Supplemental R-code is provided online.
准确估计个体内部生物学变异系数(CV)对于监测患者和设定分析性能规范都至关重要。置信区间(CI)可用于评估估计值的可靠性,因为它是估计CV不确定性的良好指标。本研究的目的是评估并建立在估计CV时构建具有正确覆盖概率和非覆盖概率的CI的方法。
基于3种个体内部效应分布模型的数据进行模拟,以评估3种构建置信区间方法的性能;基于嵌套方差分析的公式法、百分位数自抽样法和自抽样-t法。
所评估的构建CI的方法性能各异,这既取决于CV的大小,也取决于分布类型。对于所评估的模型,自抽样-t CI具有良好且稳定的性能,而基于公式的方法则更依赖于分布。百分位数自抽样法表现较差。
CI是个体内部生物学变异估计的重要组成部分。通过将自抽样-t法与CV-方差分析相结合,可以实现良好的CI覆盖概率和非覆盖概率。在线提供了补充R代码。