AN ASSESSMENT OF ESTIMATION PROCEDURES FOR MULTILEVEL MODELS WITH BINARY RESPONSES
AN ASSESSMENT OF ESTIMATION PROCEDURES FOR MULTILEVEL MODELS WITH BINARY RESPONSES
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DOI：
10.2307/2983404
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发表时间：
1995-01-01
影响因子：
2
通讯作者：
GOLDMAN, N
中科院分区：
文献类型：
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作者：
RODRIGUEZ, G;GOLDMAN, N
我们通过使用蒙特卡罗研究来评估两个可用于将多级模型拟合到二元响应数据的软件包，即 VARCL 和 ML3，该研究旨在非常接近地代表危地马拉医疗保健利用率分析中使用的数据集的实际结构。我们发现，当随机效应足够大而有趣时，软件包产生的固定效应和方差分量的估计会受到非常大的向下偏差的影响。事实上，固定效应估计并不比使用忽略数据层次结构的标准合法模型获得的估计更好。标准误差的估计似乎相当准确，并且优于通过忽略聚类获得的估计，尽管人们可能会质疑它们在存在较大偏差的情况下的效用。我们的结论是，需要针对二元响应情况开发和实施替代估计程序。
We evaluate two software packages that are available for fitting multilevel models to binary response data, namely VARCL and ML3, by using a Monte Carlo study designed to represent quite closely the actual structure of a data set used in an analysis of health care utilization in Guatemala. We find that the estimates of fixed effects and variance components produced by the software packages are subject to very substantial downward bias when the random effects are sufficiently large to be interesting. In fact, the fixed effect estimates are no better than the estimates obtained by using standard legit models that ignore the hierarchical structure of the data. The estimates of standard errors appear to be reasonably accurate and superior to those obtained by ignoring clustering, although one might question their utility in the presence of large biases. We conclude that alternative estimation procedures need to be developed and implemented for the binary response case.