Joffe Marshall M, Yang Wei Peter, Feldman Harold
Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.
Biometrics. 2012 Mar;68(1):275-86. doi: 10.1111/j.1541-0420.2011.01656.x. Epub 2011 Sep 23.
In principle, G-estimation is an attractive approach for dealing with confounding by variables affected by treatment. It has rarely been applied for estimation of the effects of treatment on failure-time outcomes. Part of this is due to artificial censoring, an analytic device which considers some subjects who actually were observed to fail as if they were censored. Artificial censoring leads to a lack of smoothness in the estimating function, which can pose problems in variance estimation and in optimization. It also can lead to failure to have solutions to the usual estimating functions, which then raises questions about the appropriate criteria for optimization. To improve performance of the optimization procedures, we consider approaches for reducing the amount of artificial censoring, propose the substitution of smooth for indicator functions, and propose the use of estimating functions scaled to a measure of the information in the data; we evaluate performance of these approaches using simulation. We also consider appropriate optimization criteria in the presence of information loss due to artificial censoring. We motivate and illustrate our approaches using observational data on the effect of erythropoietin on mortality among subjects on hemodialysis.
原则上，G估计是处理受治疗影响的变量所导致的混杂问题的一种有吸引力的方法。它很少被用于估计治疗对失效时间结局的影响。部分原因在于人为截尾，这是一种分析手段，它将一些实际观察到发生失败的受试者视为被截尾。人为截尾导致估计函数缺乏平滑性，这可能在方差估计和优化方面带来问题。它还可能导致通常的估计函数无解，进而引发关于合适的优化标准的问题。为了提高优化程序的性能，我们考虑减少人为截尾量的方法，提议用平滑函数替代指示函数，并提议使用按数据中的信息量进行缩放的估计函数；我们通过模拟评估这些方法的性能。我们还考虑在存在因人为截尾导致的信息损失的情况下合适的优化标准。我们利用关于促红细胞生成素对血液透析患者死亡率影响的观察数据来推动并阐释我们的方法。
