Semiparametric estimators for the regression coefficients in the linear transformation competing risks model with missing cause of failure
Semiparametric estimators for the regression coefficients in the linear transformation competing risks model with missing cause of failure
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DOI：
10.1093/biomet/92.4.875
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发表时间：
2005-12-01
期刊：
影响因子：
2.7
通讯作者：
Tsiatis, AA
中科院分区：
文献类型：
--
作者：
Gao, GZ;Tsiatis, AA
我们考虑在竞争风险模型中估计回归系数的问题，其中使用线性变换模型描述感兴趣原因的特定原因风险与协变量之间的关系，并且当一部分个体的失败原因随机丢失时。使用罗宾斯等人的理论。 (1994) 对于缺失数据问题和 Chen 等人的方法。 （2002）为了估计线性变换模型的回归系数，我们推导了双稳健回归系数的增强逆概率加权完整情况估计器。模拟证明了该理论在有限样本中的相关性。
We consider the problem of estimating the regression coefficients in a competing risks model, where the relationship between the cause-specific hazard for the cause of interest and covariates is described using linear transformation models and when cause of failure is missing at random for a subset of individuals. Using the theory of Robins et al. (1994) for missing data problems and the approach of Chen et al. (2002) for estimating regression coefficients for linear transformation models, we derive augmented inverse probability weighted complete-case estimators for the regression coefficients that are doubly robust. Simulations demonstrate the relevance of the theory in finite samples.