| 梁融,周舒冬,李丽霞,张俊国,郜艳晖.层次结构定量数据组内相关系数的非参数Bootstrap估计[J].Chinese journal of Epidemiology,2013,34(9):927-930 |
| 层次结构定量数据组内相关系数的非参数Bootstrap估计 |
| Non-parametric Bootstrap estimation on the intraclass correlation coefficient generated from quantitative hierarchical data |
| Received:May 13, 2013 |
| DOI： |
| KeyWord: 层次结构数据|组内相关系数|可信区间|Bootstrap自抽样 |
| English Key Word: Hierarchical data|Intraclass correlation coefficient|Confidence interval|Bootstrapping |
| FundProject:广东省自然科学基金(10151022401000018) |
| Author Name | Affiliation | E-mail | | LIANG Rong | Department of Epidemiology and Biostatistics, Guangdong Pharmaceutical Uniersity, Guangzhou 510310. China | | | ZHOU Shu=dong | Department of Epidemiology and Biostatistics, Guangdong Pharmaceutical Uniersity, Guangzhou 510310. China | | | LI Li-xia | Department of Epidemiology and Biostatistics, Guangdong Pharmaceutical Uniersity, Guangzhou 510310. China | | | ZHANG Jun-guo | Department of Epidemiology and Biostatistics, Guangdong Pharmaceutical Uniersity, Guangzhou 510310. China | | | GAO Yan- hui | Department of Epidemiology and Biostatistics, Guangdong Pharmaceutical Uniersity, Guangzhou 510310. China | gao_yanhui@163．Com |
|
| Hits: 3618 |
| Download times: 1528 |
| Abstract: |
| 探讨Bootstrap自抽样如何在层次结构数据中实现，为组内相关系数(ICC)可信区间的计算方法提供选择。文中利用混合效应模型估计重复测量数据和两阶段抽样数据的ICC及利用Bootstrap法估计ICC的可信区间，比较不同的自抽样模式下ICC可信区间结果。重复测量实例 结果显示Bootstrap整群抽样估计的可信区间包含ICC真值，如忽视数据的层次结构特征， Bootstrap方法得到无效的可信区间估计；两阶段抽样实例结果显示整群Bootstrap自抽样方法估计的ICC均数与原样本ICC偏差最小，可信区间宽泛。表明对层次结构数据进行Bootstrap自抽样，需考虑数据的产生机制，即高水平Bootstrap自抽样的统计量估计更接近原样本统计量。 |
| English 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 Cls 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． |
|
View Fulltext
Html FullText
View/Add Comment Download reader |
| Close |