Intraclass correlation for reliability assessment: the introduction of a validated program in SAS (ICC6)

Abstract

Reliability refers to how measurements can produce consistent results and are crucial for any scientific research measurement. Intraclass correlation coefficient (ICC) is the most widely used method to determine the reproducibility of measurements of various statistical techniques. Calculated ICC and its confidence interval that reveal the underlying sampling distribution may help detect an experimental method's ability to identify systematic differences between research participants in a test. This study aimed to introduce a new SAS macro, ICC6, for calculating different ICC forms and their confidence intervals. A SAS macro that employs the PROC GLM procedure in SAS was created to generate two-way random effects (ANOVA) estimates. A simulated dataset was used to input the macro to calculate the point estimates for different ICCs. The ICC forms' upper and lower confidence interval limits were calculated using the F statistics distribution. Our SAS macro provides a complete set of various ICC forms and their confidence intervals. A validation analysis using commercial software packages STATA and SPSS delivered identical results. A development of SAS methodology using publicly available statistical approaches in estimating six distinct forms of ICC and their confidence intervals has been reported in this article. This work is an extension of general methodology supported by a few other statistical software packages to SAS.