Bootstrapped inference for variance parameters, measures of heterogeneity and random effects in multilevel logistic regression models
Bootstrapped inference for variance parameters, measures of heterogeneity and random effects in multilevel logistic regression models
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DOI：
10.1080/00949655.2020.1797738
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发表时间：
2020-08-14
影响因子：
1.2
通讯作者：
Leckie, George
中科院分区：
文献类型：
--
作者：
Austin, Peter C.;Leckie, George
我们使用蒙特卡罗模拟来评估三种引导程序用于多水平数据的性能（参数自举法、残差自举法和非参数自举法），用于在使用多水平logistic回归模型时估计聚类变异和异质性的三个度量的抽样变异：随机效应分布的方差、方差分配系数（此处相当于组内相关系数）和中位数比值比。我们还描述了一种新的参数引导程序来估计预测的集群特定的随机效应的标准误。我们的研究结果表明，参数和残差自助法，在一般情况下，应该被用来估计集群变异和异质性的关键措施的抽样变异。估计预测的集群特定的随机效应的标准误的新的参数引导程序的性能往往超过基于模型的估计。
We used Monte Carlo simulations to assess the performance of three bootstrap procedures for use with multilevel data (the parametric bootstrap, the residuals bootstrap, and the nonparametric bootstrap) for estimating the sampling variation of three measures of cluster variation and heterogeneity when using a multilevel logistic regression model: the variance of the distribution of the random effects, the variance partition coefficient (equivalent here to the intraclass correlation coefficient), and the median odds ratio. We also described a novel parametric bootstrap procedure to estimate the standard errors of the predicted cluster-specific random effects. Our results suggest that the parametric and residuals bootstrap should, in general, be used to estimate the sampling variation of key measures of cluster variation and heterogeneity. The performance of the novel parametric bootstrap procedure for estimating the standard errors of predicted cluster-specific random effects tended to exceed that of the model-based estimates.