Quantile Regression for Competing Risks Data with Missing Cause of Failure.
Quantile Regression for Competing Risks Data with Missing Cause of Failure.
复制标题
DOI：
10.5705/ss.2010.093
复制
发表时间：
2012-04-01
影响因子：
1.4
通讯作者：
Gilbert PB
中科院分区：
文献类型：
--
作者：
Sun Y;Wang HJ;Gilbert PB
本文考虑了失效类型可能缺失时竞争风险数据的广义线性分位数回归。在失效类型为随机缺失的假设下，讨论了回归系数的两种估计方法：逆概率加权完全情况估计和增广逆概率加权估计.所提出的估计程序利用补充辅助变量来预测缺失的故障类型，并告知其分布。导出了这两种估计的渐近性质，并比较了它们的渐近效率。我们表明，增广估计是更有效的，并具有双重的鲁棒性对误指定的模型缺失或故障类型。渐近协方差的估计使用的估计函数的局部函数线性。有限样本性能的估计程序进行了评估，通过模拟研究。这些方法被应用于分析“玛希”试验数据，以调查配方奶粉与母乳喂养加延长婴儿齐多夫定预防对博茨瓦纳艾滋病毒感染母亲所生婴儿的艾滋病毒相关死亡的影响。
This paper considers generalized linear quantile regression for competing risks data when the failure type may be missing. Two estimation procedures for the regression co-efficients, including an inverse probability weighted complete-case estimator and an augmented inverse probability weighted estimator, are discussed under the assumption that the failure type is missing at random. The proposed estimation procedures utilize supplemental auxiliary variables for predicting the missing failure type and for informing its distribution. The asymptotic properties of the two estimators are derived and their asymptotic efficiencies are compared. We show that the augmented estimator is more efficient and possesses a double robustness property against misspecification of either the model for missingness or for the failure type. The asymptotic covariances are estimated using the local functional linearity of the estimating functions. The finite sample performance of the proposed estimation procedures are evaluated through a simulation study. The methods are applied to analyze the ‘Mashi’ trial data for investigating the effect of formula-versus breast-feeding plus extended infant zidovudine prophylaxis on HIV-related death of infants born to HIV-infected mothers in Botswana.
登录
查看更多内容
影响因子：
1.9
作者：
PRENTICE, RL;KALBFLEISCH, JD;BRESLOW, NE
通讯作者：
BRESLOW, NE
影响因子：
2.7
作者：
Gao, GZ;Tsiatis, AA
通讯作者：
Tsiatis, AA
影响因子：
5.1
作者：
BEAUDRY, M;DUFOUR, R;MARCOUX, S
通讯作者：
MARCOUX, S
影响因子：
1.9
作者：
Bang, H;Tsiatis, AA
通讯作者：
Tsiatis, AA
影响因子：
3.7
作者：
YING, Z;JUNG, SH;WEI, LJ
通讯作者：
WEI, LJ