Covariates missing by design: Comparison of the efficient score to other weighted methods
Covariates missing by design: Comparison of the efficient score to other weighted methods
复制标题
DOI：
10.1002/sim.2686
复制
发表时间：
2007-05-10
影响因子：
2
通讯作者：
Weissfeld, Lisa
中科院分区：
文献类型：
--
作者：
D'Angelo, Gina;Weissfeld, Lisa
本文讨论了缺失协变量数据的Logistic回归模型的建模。本文的目的是评估两阶段设计中逻辑回归的有效得分的性质。仿真研究表明，当缺失协变量与其替代变量之间的相关性较高或抽样比例较小时，有效得分法比其他两种伪似然方法更有效。这些方法与国家肾母细胞瘤研究组的数据进行说明。从例子中的结果证实了模拟研究的结果，除了伪似然方法产生更可靠的估计比加权伪似然方法。版权所有（c）2006约翰威利父子有限公司。
This paper addresses the modelling of missing covariate data with the logistic regression model. The aim of this paper is to evaluate the properties of an efficient score for logistic regression in a two-phase design. Simulation studies show that the efficient score is more efficient than two other pseudo-likelihood methods when the correlation between the missing covariate and its surrogate is high or the sampling proportion is small. These methods are illustrated with data from the National Wilms Tumor Study Group. Results from the example confirm the simulation study findings with the exception that the pseudo-likelihood approach produces more reliable estimates than the weighted pseudo-likelihood approach. Copyright (c) 2006 John Wiley & Sons, Ltd.