Bootstrap Estimate of Bias for Intraclass Correlation.

Abstract

The estimates of intraclass correlations are known to be biased, but there are few analytical ways to assess the amount of bias. The analytical approach requires the normality assumption to estimate bias. Bootstrap requires no such assumption and can, therefore, be used to estimate bias, regardless of the model assumption. We utilize cluster bootstrapping to calculate the bias in estimating the intraclass correlation. A well-known dataset is provided to illustrate the bias estimation in a typical study design of intraclass correlation, and its implications for other study designs are also discussed.