Non-parametric Bootstrap estimation on the intraclass correlation coefficient generated from quantitative hierarchical data / 中华流行病学杂志

ABSTRACT

This paper aims to achieve Bootstraping in hierarchical data and to provide a method for the estimation on confidence interval (CI) of intraclass correlation coefficient (ICC).First,we utilize the mixed-effects model to estimate data from ICC of repeated measurement and from the two-stage sampling.Then,we use Bootstrap method to estimate CI from related ICCs.Finally,the influences of different Bootstraping strategies to ICC' s CIs are compared.The repeated measurement instance show that the CI of cluster Bootsraping containing the true ICC value.However,when ignoring the hierarchy characteristics of data,the random Bootsraping method shows that it has the invalid CI.Result from the two-stage instance shows that bias obsered between cluster Bootstraping's ICC means while the ICC of the original sample is the smallest,but with wide CI.It is necessary to consider the structure of data as important,when hierarchical data is being resampled.Bootstrapping seems to be better on the higher than that on lower levels.