Abstract #301074

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JSM 2003 Abstract #301074
Activity Number: 420
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #301074
Title: Intraclass Correlation Coefficients of Hierarchical Binomial Data
Author(s): Shiquan Ren*+ and Shenghan Lai and Shuqin Yang
Companies: Johns Hopkins University and Johns Hopkins University and Sichuan University
Address: 615 North Wolfe St., Baltimore, MD, 21205,
Keywords: multilevel generalized linear model ; intraclass correlation ; bootstrap methods ; hierarchical data
Abstract:

Intraclass correlation coefficients are designed to assess the consistency or conformity between two or more quantitative measurements. When multi-stage cluster sampling is implemented, the methods to estimate intraclass correlations of binomial-distributed outcomes within a cluster are not readily available. Since the distribution of intraclass correlation coefficients could be complicated or unspecified, we proposed to estimate standard error and confidence interval within a framework of multilevel generalized linear model using bootstrap method. We compared the results derived from parametric bootstrap method with those from nonparametric bootstrap method, and found that the nonparametric bootstrap method is more robust. In terms of the nonparametric bootstrap sampling, we demonstrated that the effectiveness of sampling on the highest level is more satisfactory than that of lower levels by using an example in which a survey data on viral hepatitis in China is analyzed.

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