{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T12:30:55Z","timestamp":1788870655025,"version":"build-2803163510"},"reference-count":9,"publisher":"Wiley","issue":"20","license":[{"start":{"date-parts":[[2006,1,13]],"date-time":"2006-01-13T00:00:00Z","timestamp":1137110400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2006,1,13]],"date-time":"2006-01-13T00:00:00Z","timestamp":1137110400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"},{"start":{"date-parts":[[2006,1,13]],"date-time":"2006-01-13T00:00:00Z","timestamp":1137110400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"National Institute on Drug Abuse of U.S.A.","award":["DA12777"],"award-info":[{"award-number":["DA12777"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["70103007"],"award-info":[{"award-number":["70103007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Social Science Foundation of China","award":["02CTJ001"],"award-info":[{"award-number":["02CTJ001"]}]}],"content-domain":{"domain":["wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2006,10,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Intraclass correlation coefficients are designed to assess consistency or conformity between two or more quantitative measurements. When multistage cluster sampling is implemented, no methods are readily available to estimate intraclass correlations of binomial\u2010distributed outcomes within a cluster. Because statistical distribution of the intraclass correlation coefficients could be complicated or unspecified, we propose using a bootstrap method to estimate the standard error and confidence interval within the framework of a multilevel generalized linear model. We compared the results derived from a parametric bootstrap method with those from a non\u2010parametric bootstrap method and found that the non\u2010parametric method is more robust. For non\u2010parametric bootstrap sampling, we showed that the effectiveness of sampling on the highest level is greater than that on lower levels; to illustrate the effectiveness, we analyse survey data in China and do simulation studies. 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