Bayesian methods for missing covariates in cure rate models
Bayesian methods for missing covariates in cure rate models
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DOI：
10.1023/a:1014835522957
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发表时间：
2002-06-01
影响因子：
1.3
通讯作者：
Lipsitz, SR
中科院分区：
文献类型：
--
作者：
Chen, MH;Ibrahim, JG;Lipsitz, SR
我们针对具有治愈比例的一类新型半参数生存模型中协变量数据缺失的情况提出了贝叶斯推断方法。我们允许缺失的协变量为分类变量或连续变量，并为协变量指定一个参数分布，该分布写成一系列一维条件分布。我们始终假设缺失的协变量是随机缺失（MAR）的。我们针对回归系数以及由协变量分布产生的参数提出了一类信息丰富的联合先验分布。所提出的先验类别在恢复缺失协变量的信息方面被证明是有用的，特别是在缺失数据比例较大的情况下。对所提出的先验分布以及由此产生的后验分布的性质进行了检验。此外，还提出了用于敏感性分析和检验特定模型拟合优度的模型检验技术。具体而言，我们扩展了条件预测纵坐标（CPO）统计量，以在存在协变量数据缺失的情况下评估拟合优度。实现了使用吉布斯采样器的计算技术。对一个涉及黑色素瘤癌症临床试验的真实数据集进行了检验，以演示该方法。
We propose methods for Bayesian inference for missing covariate data with a novel class of semi-parametric survival models with a cure fraction. We allow the missing covariates to be either categorical or continuous and specify a parametric distribution for the covariates that is written as a sequence of one dimensional conditional distributions. We assume that the missing covariates are missing at random (MAR) throughout. We propose an informative class of joint prior distributions for the regression coefficients and the parameters arising from the covariate distributions. The proposed class of priors are shown to be useful in recovering information on the missing covariates especially in situations where the missing data fraction is large. Properties of the proposed prior and resulting posterior distributions are examined. Also, model checking techniques are proposed for sensitivity analyses and for checking the goodness of fit of a particular model. Specifically, we extend the Conditional Predictive Ordinate (CPO) statistic to assess goodness of fit in the presence of missing covariate data. Computational techniques using the Gibbs sampler are implemented. A real data set involving a melanoma cancer clinical trial is examined to demonstrate the methodology.