Evaluating multiple surrogate markers with censored data.
Evaluating multiple surrogate markers with censored data.
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作者：
Parast L;Cai T;Tian L
替代标记物的使用提供了减少所需随访时间长度和/或检查干预或治疗有效性的随机试验成本的机会。有许多可用的方法来评估一个单一的替代标记物的效用，包括参数和非参数的方法。然而，随着替代标记的维数增加，由于维数灾难，完全非参数过程变得不可行。在本文中，我们定义了一个数量来评估多个替代标志物的价值，在一个时间到事件的结果设置，并提出了一个强大的估计方法删失数据。我们关注的是在某个标志性时间t0测量的替代标志物，t0发生在研究结束之前。我们的方法是基于降维过程的一个选项，将权重，以防止潜在的误指定的工作模型，从而在三个不同的建议估计，其中两个可以被证明是双鲁棒的。我们研究有限样本性能的估计在各种情况下，使用模拟研究。我们说明了估计和推理程序，使用糖尿病预防计划（DPP）的数据来检查糖尿病的多个潜在的替代标志物。
The utilization of surrogate markers offers the opportunity to reduce the length of required follow-up time and/or costs of a randomized trial examining the effectiveness of an intervention or treatment. There are many available methods for evaluating the utility of a single surrogate marker including both parametric and nonparametric approaches. However, as the dimension of the surrogate marker increases, a completely nonparametric procedure becomes infeasible due to the curse of dimensionality. In this paper, we define a quantity to assess the value of multiple surrogate markers in a time-to-event outcome setting and propose a robust estimation approach for censored data. We focus on surrogate markers that are measured at some landmark time, t0, which occurs earlier than the end of the study. Our approach is based on a dimension reduction procedure with an option to incorporate weights to guard against potential misspecification of the working model, resulting in three different proposed estimators, two of which can be shown to be double robust. We examine the finite sample performance of the estimators under various scenarios using a simulation study. We illustrate the estimation and inference procedures using data from the Diabetes Prevention Program(DPP) to examine multiple potential surrogate markers for diabetes.
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影响因子：
158.5
作者：
Knowler, WC;Barrett-Connor, E;Nathan, DM
通讯作者：
Nathan, DM
影响因子：
1.9
作者：
Alonso, Ariel;Van der Elst, Wim;Burzykowski, Tomasz
通讯作者：
Burzykowski, Tomasz
影响因子：
2
作者：
HJORT, NL
通讯作者：
HJORT, NL
DOI：
10.1002/jbm.b.34149
发表时间：
2019-04-01
影响因子：
3.4
作者：
Liu, Jue;Zhou, Xiongwen;Ruan, Jianming
通讯作者：
Ruan, Jianming
影响因子：
2
作者：
Parast L;McDermott MM;Tian L
通讯作者：
Tian L