Dukes Oliver, Martinussen Torben, Tchetgen Tchetgen Eric J, Vansteelandt Stijn
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 S9, Ghent 9000, Belgium.
Department of Biostatistics, University of Copenhagen, Øster Farimagsgade 5B, 1014 Copenhagen K, Denmark.
Biometrics. 2019 Mar;75(1):100-109. doi: 10.1111/biom.12943. Epub 2018 Aug 22.
The estimation of conditional treatment effects in an observational study with a survival outcome typically involves fitting a hazards regression model adjusted for a high-dimensional covariate. Standard estimation of the treatment effect is then not entirely satisfactory, as the misspecification of the effect of this covariate may induce a large bias. Such misspecification is a particular concern when inferring the hazard difference, because it is difficult to postulate additive hazards models that guarantee non-negative hazards over the entire observed covariate range. We therefore consider a novel class of semiparametric additive hazards models which leave the effects of covariates unspecified. The efficient score under this model is derived. We then propose two different estimation approaches for the hazard difference (and hence also the relative chance of survival), both of which yield estimators that are doubly robust. The approaches are illustrated using simulation studies and data on right heart catheterization and mortality from the SUPPORT study.
在具有生存结局的观察性研究中，条件治疗效果的估计通常涉及拟合一个针对高维协变量进行调整的风险回归模型。由于该协变量效应的错误设定可能会导致较大偏差，因此治疗效果的标准估计并不完全令人满意。在推断风险差异时，这种错误设定尤其令人担忧，因为很难假定能保证在整个观察到的协变量范围内风险非负的相加风险模型。因此，我们考虑一类新的半参数相加风险模型，这类模型不明确协变量的效应。推导了该模型下的有效得分。然后，我们针对风险差异（进而也是生存的相对机会）提出了两种不同的估计方法，这两种方法得到的估计量都是双重稳健的。通过模拟研究以及来自SUPPORT研究的右心导管插入术和死亡率数据对这些方法进行了说明。
