Robust extraction of covariate information to improve estimation efficiency in randomized trials
Robust extraction of covariate information to improve estimation efficiency in randomized trials
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DOI：
10.1002/sim.4301
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发表时间：
2011-08-30
影响因子：
2
通讯作者：
van der Laan, Mark J.
中科院分区：
文献类型：
--
作者：
Moore, Kelly L.;Neugebauer, Romain;van der Laan, Mark J.
在随机试验中，研究人员通常依赖于每个治疗组中平均结果的未经调整的估计来得出因果推断。统计学家强调了在随机试验中通过协变量调整可以提高效率，重点放在涉及线性模型的问题上。尽管最近在理论上取得了进展，但基于两个主要原因，人们一直不愿对协变量进行调整：(1)基于条件Logistic回归模型的协变量调整估计已被证明不那么精确；(2)对操纵协变量调整的模型选择过程以获得有利结果的机会的担忧。在本文中，我们解决了这两个问题，并总结了最近的理论结果，以此为基础，提出了在治疗概率为50%的两组试验中，目标最大似然估计框架下的协变量调整的一般方法。所提出的方法提供了代表人群水平的治疗效果的真实因果参数的估计。它与基于条件Logistic模型的估计进行了比较，后者只提供了亚组水平的治疗效果的估计，而不是边际(无条件)治疗效果的估计。我们提供了一个明确的标准来确定是否可以通过协变量调整实现效率的提高，而不是未调整的方法。我们使用来自安慰剂对照的4期研究的重新抽样的临床试验数据集来说明我们的策略。结果表明，即使在二元结果的情况下，通过协变量调整可以提高统计能力，也可以实现效率的提高。版权所有(C)2011 John Wiley&Sons，Ltd.
In randomized trials, investigators typically rely upon an unadjusted estimate of the mean outcome within each treatment arm to draw causal inferences. Statisticians have underscored the gain in efficiency that can be achieved from covariate adjustment in randomized trials with a focus on problems involving linear models. Despite recent theoretical advances, there has been a reluctance to adjust for covariates based on two primary reasons: (i) covariate-adjusted estimates based on conditional logistic regression models have been shown to be less precise and (ii) concern over the opportunity to manipulate the model selection process for covariate adjustments to obtain favorable results. In this paper, we address these two issues and summarize recent theoretical results on which is based a proposed general methodology for covariate adjustment under the framework of targeted maximum likelihood estimation in trials with two arms where the probability of treatment is 50%. The proposed methodology provides an estimate of the true causal parameter of interest representing the population-level treatment effect. It is compared with the estimates based on conditional logistic modeling, which only provide estimates of subgroup-level treatment effects rather than marginal (unconditional) treatment effects. We provide a clear criterion for determining whether a gain in efficiency can be achieved with covariate adjustment over the unadjusted method. We illustrate our strategy using a resampled clinical trial dataset from a placebo controlled phase 4 study. Results demonstrate that gains in efficiency can be achieved even with binary outcomes through covariate adjustment leading to increased statistical power. Copyright (C) 2011 John Wiley & Sons, Ltd.