Locally Efficient Semiparametric Estimators for Proportional Hazards Models with Measurement Error.
Locally Efficient Semiparametric Estimators for Proportional Hazards Models with Measurement Error.
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DOI：
10.1111/sjos.12191
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发表时间：
2016-06
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通讯作者：
Song X
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文献类型：
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作者：
Xu Y;Li Y;Song X
在协变量存在测量误差的情况下，提出了一类新的比例风险模型的半参数估计，其中基线风险函数、截尾时间风险函数和真实协变量的分布被认为是未知的无穷维参数.我们估计模型的组件，通过求解估计方程的基础上的半参数有效的分数下的一系列限制性模型，其中的对数的危险函数近似的降秩回归样条。建议的估计是局部有效的，在这个意义上，估计是半参数有效的，如果分布的易错协变量是正确指定的，仍然是一致的，渐近正态的，如果分布是错误的。我们的模拟研究表明，所提出的估计有较小的偏差和方差比竞争的方法。我们进一步说明了一个真实的应用在艾滋病毒临床试验的新方法。
We propose a new class of semiparametric estimators for proportional hazards models in the presence of measurement error in the covariates, where the baseline hazard function, the hazard function for the censoring time, and the distribution of the true covariates are considered as unknown infinite dimensional parameters. We estimate the model components by solving estimating equations based on the semiparametric efficient scores under a sequence of restricted models where the logarithm of the hazard functions are approximated by reduced rank regression splines. The proposed estimators are locally efficient in the sense that the estimators are semiparametrically efficient if the distribution of the error-prone covariates is specified correctly, and are still consistent and asymptotically normal if the distribution is misspecified. Our simulation studies show that the proposed estimators have smaller biases and variances than competing methods. We further illustrate the new method with a real application in an HIV clinical trial.