Bembom Oliver, van der Laan Mark J
Division of Biostatistics, University of California at Berkeley, CA, USA.
Stat Med. 2008 Aug 30;27(19):3689-716. doi: 10.1002/sim.3268.
In this paper, we argue that causal effect models for realistic individualized treatment rules represent an attractive tool for analyzing sequentially randomized trials. Unlike a number of methods proposed previously, this approach does not rely on the assumption that intermediate outcomes are discrete or that models for the distributions of these intermediate outcomes given the observed past are correctly specified. In addition, it generalizes the methodology for performing pairwise comparisons between individualized treatment rules by allowing the user to posit a marginal structural model for all candidate treatment rules simultaneously. This is particularly useful if the number of such rules is large, in which case an approach based on individual pairwise comparisons would be likely to suffer from too much sampling variability to provide an informative answer. In addition, such causal effect models represent an interesting alternative to methods previously proposed for selecting an optimal individualized treatment rule in that they immediately give the user a sense of how the optimal outcome is estimated to change in the neighborhood of the identified optimum. We discuss an inverse-probability-of-treatment-weighted (IPTW) estimator for these causal effect models, which is straightforward to implement using standard statistical software, and develop an approach for constructing valid asymptotic confidence intervals based on the influence curve of this estimator. The methodology is illustrated in two simulation studies that are intended to mimic an HIV/AIDS trial.
在本文中，我们认为用于现实个体化治疗规则的因果效应模型是分析序贯随机试验的一个有吸引力的工具。与先前提出的许多方法不同，这种方法不依赖于中间结果是离散的假设，也不依赖于给定观察到的过去情况时这些中间结果分布模型被正确设定的假设。此外，它通过允许用户同时为所有候选治疗规则设定一个边际结构模型，推广了在个体化治疗规则之间进行成对比较的方法。如果此类规则的数量很大，这一点尤其有用，在这种情况下，基于逐个成对比较的方法可能会因抽样变异性过大而无法提供有价值的答案。此外，此类因果效应模型是先前提出的用于选择最优个体化治疗规则的方法的一个有趣替代方案，因为它们能让用户立刻了解到在已确定的最优值附近最优结果估计值是如何变化的。我们讨论了这些因果效应模型的逆概率治疗权重（IPTW）估计器，它使用标准统计软件很容易实现，并基于该估计器的影响曲线开发了一种构建有效渐近置信区间的方法。该方法在两项旨在模拟艾滋病试验的模拟研究中得到了说明。
