Leveraging prognostic baseline variables to gain precision in randomized trials.
Leveraging prognostic baseline variables to gain precision in randomized trials.
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DOI：
10.1002/sim.6507
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发表时间：
2015-08-15
影响因子：
2
通讯作者：
Rosenblum M
中科院分区：
文献类型：
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作者：
Colantuoni E;Rosenblum M
我们专注于估计随机试验中的平均治疗效果。如果基线变量与结果相关，则适当调整这些变量可以提高精度。一个例子是协方差分析（ANCOVA）估计量，当结果是连续的，关注的数量是比较治疗与对照的平均结果的差异，并且使用仅具有主效应的线性模型时，该估计量适用。ANCOVA保证至少与标准的未调整估计量一样精确，渐近，在无参数模型假设下，也是局部半参数有效的。最近，已经开发了几种估计器，其将这些期望的属性扩展到允许任何实值结果（例如，二进制或计数），除了平均结果差异之外的对比（例如相对风险），以及基于一大类广义线性模型（包括逻辑回归）的估计量。据我们所知，我们给出了第一个模拟研究的背景下，比较这些估计的随机试验。此外，我们的模拟不是基于参数模型;相反，我们的模拟是基于中风和艾滋病毒的已完成随机试验的恢复数据，以评估估计器在现实情况下的性能。我们提供了实用的指导，当这些估计可能提供大量的精度增益，并描述了一个快速的评估方法，使临床研究人员，以确定这些估计是否可能是有用的，在其特定的试验环境。
We focus on estimating the average treatment effect in a randomized trial. If baseline variables are correlated with the outcome, then appropriately adjusting for these variables can improve precision. An example is the analysis of covariance (ANCOVA) estimator, which applies when the outcome is continuous, the quantity of interest is the difference in mean outcomes comparing treatment versus control, and a linear model with only main effects is used. ANCOVA is guaranteed to be at least as precise as the standard unadjusted estimator, asymptotically, under no parametric model assumptions and also is locally semiparametric efficient. Recently, several estimators have been developed that extend these desirable properties to more general settings that allow any real-valued outcome (e.g., binary or count), contrasts other than the difference in mean outcomes (such as the relative risk), and estimators based on a large class of generalized linear models (including logistic regression). To the best of our knowledge, we give the first simulation study in the context of randomized trials that compares these estimators. Furthermore, our simulations are not based on parametric models; instead, our simulations are based on resampling data from completed randomized trials in stroke and HIV in order to assess estimator performance in realistic scenarios. We provide practical guidance on when these estimators are likely to provide substantial precision gains and describe a quick assessment method that allows clinical investigators to determine whether these estimators could be useful in their specific trial contexts.