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首页> 外文期刊>Statistics in medicine >Non-parametric bootstrap confidence intervals for the intraclass correlation coefficient.
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Non-parametric bootstrap confidence intervals for the intraclass correlation coefficient.

机译：类内相关系数的非参数自举置信区间。

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摘要

The intraclass correlation coefficient rho plays a key role in the design of cluster randomized trials. Estimates of rho obtained from previous cluster trials and used to inform sample size calculation in planned trials may be imprecise due to the typically small numbers of clusters in such studies. It may be useful to quantify this imprecision. This study used simulation to compare different methods for assigning bootstrap confidence intervals to rho for continuous outcomes from a balanced design. Data were simulated for combinations of numbers of clusters (10, 30, 50), intraclass correlation coefficients (0.001, 0.01, 0.05, 0.3) and outcome distributions (normal, non-normal continuous). The basic, bootstrap-t, percentile, bias corrected and bias corrected accelerated bootstrap intervals were compared with new methods using the basic and bootstrap-t intervals applied to a variance stabilizing transformation of rho. The standard bootstrap methods provided coverage levels for 95 per cent intervals that were markedly lower than the nominal level for data sets with only 10 clusters, and only provided close to 95 per cent coverage when there were 50 clusters. Application of the bootstrap-t method to the variance stabilizing transformation of rho improved upon the performance of the standard bootstrap methods, providing close to nominal coverage.
机译：组内相关系数rho在聚类随机试验的设计中起关键作用。由于此类研究中簇的数量通常很少，因此从先前的簇试验获得的rho估计值并不能用于计划试验中的样本量计算，因此可能不准确。量化这种不精确度可能很有用。这项研究使用仿真来比较将自举置信区间分配给rho的不同方法，以实现平衡设计的连续结果。模拟数据的簇数（10、30、50），组内相关系数（0.001、0.01、0.05、0.3）和结果分布（正常，非正常连续）的组合。将基本，bootstrap-t，百分位数，偏差校正和偏差校正的加速bootstrap间隔与新方法进行了比较，这些新方法使用了应用于rho的方差稳定变换的basic和bootstrap-t间隔。标准的引导程序方法提供了95％的时间间隔覆盖率水平，这明显低于只有10个群集的数据集的标称水平，并且只有50个群集时才提供接近95％的覆盖率。 bootstrap-t方法在rho的方差稳定转换中的应用在标准bootstrap方法的性能上得到了改进，提供了接近标称的覆盖范围。

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