The Impact of Sample Size and Other Factors When Estimating Multilevel Logistic Models
The Impact of Sample Size and Other Factors When Estimating Multilevel Logistic Models
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DOI：
10.1080/00220973.2015.1027805
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发表时间：
2016-04
期刊：
影响因子：
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通讯作者：
J. Schoeneberger
中科院分区：
文献类型：
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作者：
J. Schoeneberger
使用二元多水平模型的研究研究的设计必须包括多种因素的知识，包括估计方法、方差分量大小或预测因子的数量，以及样本大小。这项蒙特卡罗研究使用SAS软件检验了随机效应二元结果多水平模型在不同估计方法、水平1和水平2样本量、结果患病率、方差分量大小和预测因子数量下的性能。统计能力的平均估计主要受两个水平的样本大小的影响。此外，方差分量的大小和估计方法对非正定随机效应协方差矩阵的可信区间覆盖和宽度以及似然概率也有影响。探讨了这些因素和其他因素与各种模型性能结果之间的相互作用。
The design of research studies utilizing binary multilevel models must necessarily incorporate knowledge of multiple factors, including estimation method, variance component size, or number of predictors, in addition to sample sizes. This Monte Carlo study examined the performance of random effect binary outcome multilevel models under varying methods of estimation, level-1 and level-2 sample size, outcome prevalence, variance component sizes, and number of predictors using SAS software. Mean estimates of statistical power were influenced primarily by sample sizes at both levels. In addition, confidence interval coverage and width and the likelihood of nonpositive definite random effect covariance matrices were impacted by variance component size and estimation method. The interactions of these and other factors with various model performance outcomes are explored.