Semiparametric models for missing covariate and response data in regression models
Semiparametric models for missing covariate and response data in regression models
复制标题
DOI：
10.1111/j.1541-0420.2005.00438.x
复制
发表时间：
2006-03-01
期刊：
影响因子：
1.9
通讯作者：
Ibrahim, JG
中科院分区：
文献类型：
--
作者：
Chen, QX;Ibrahim, JG
我们考虑一类针对协变量分布以及缺失协变量和/或响应数据的缺失数据机制的半参数模型，适用于包括广义线性模型和广义线性混合模型在内的一般回归模型类别。我们考虑了可忽略和不可忽略的缺失协变量和/或响应数据。所提出的半参数模型可被视为对缺失协变量分布和/或缺失数据机制的模型误设的一种敏感性分析。该半参数模型由针对协变量分布和/或缺失数据机制的广义相加模型（GAM）组成。惩罚回归样条被用于将广义相加模型表示为广义线性混合效应模型，其中相应随机效应的方差为在半参数模型和参数模型之间进行选择提供了一个直观的指标。然后通过期望最大化（EM）算法获得最大似然估计。通过模拟展示了该方法，并且使用所提出的方法对来自黑色素瘤癌症临床试验的一个真实数据集进行了分析。
We consider a class of semiparametric models for the covariate distribution and missing data mechanism for missing covariate and/or response data for general classes of regression models including generalized linear models and generalized linear mixed models. Ignorable and nonignorable missing covariate and/or response data are considered. The proposed semiparametric model can be viewed as a sensitivity analysis for model misspecification of the missing covariate distribution and/or missing data mechanism. The semiparametric model consists of a generalized additive model (GAM) for the covariate distribution and/or missing data mechanism. Penalized regression splines are used to express the GAMs as a generalized linear mixed effects model, in which the variance of the corresponding random effects provides an intuitive index for choosing between the semiparametric and parametric model. Maximum likelihood estimates are then obtained via the EM algorithm. Simulations are given to demonstrate the methodology, and a real data set from a melanoma cancer clinical trial is analyzed rising the proposed methods.