Effect of sample size and the traditional parametric, nonparametric, and robust methods on the establishment of reference intervals: Evidence from real world data

Abstract

Sample size and statistical methods are critical for establishing reference intervals (RIs) but they tend to be overlooked. In this study, we used R (3.6.3) to stratify the reference individuals by sex, and then stratified them using the random sampling method. Fourteen sub-data sets with a sample size of 40, 80, 120, 160, 200, 500, 800, 1000, 1500, 2000, 2500, 3000, 3500, and 4000 were extracted, respectively. The sex ratios of all sub-data sets were 1:1. Transformed parametric (using log transformation), nonparametric, and robust approaches as described in the Clinical and Laboratory Standards Institute guidelines were adopted to establish the RIs and the 90% confidence interval of the thyroid-stimulating hormone (TSH) using data from the sub-data sets. The Bland-Altman plot was used to evaluate the consistency of the upper and lower limits of the RIs established using the three methods. The upper and lower limits of TSH RI tended to be stable starting from the data set with a sample size of 1500. The RIs established using the three methods were more consistent when using a sample size greater than or equal to 2000.