On the covariate-adjusted estimation for an overall treatment difference with data from a randomized comparative clinical trial
On the covariate-adjusted estimation for an overall treatment difference with data from a randomized comparative clinical trial
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DOI：
10.1093/biostatistics/kxr050
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发表时间：
2012-04-01
期刊：
影响因子：
2.1
通讯作者：
Wei, Lee-Jen
中科院分区：
文献类型：
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作者：
Tian, Lu;Cai, Tianxi;Wei, Lee-Jen
为了利用随机比较临床研究的数据估计总体治疗差异，通常使用基线协变量来提高估计精度。使用标准的协方差分析技术来推断这种平均处理差异可能是不合适的，特别是当拟合的模型是非线性的时候。另一方面，最近由Zhang等人研究的新的增强程序(2008。使用辅助协变量提高随机临床试验中推论的效率。生物识别)是相当灵活的。然而，通常情况下，如何有效地选择协变量进行增强并不是很清楚。过度调整的估计值可能会夸大方差，在某些情况下是有偏差的。此外，通过忽略变量选择过程中的抽样变化而进行的标准推理过程的结果可能是无效的。在本文中，我们首先提出了一种估计过程，它直接用协变量来增加简单的处理对比度估计。新方案与上述增广方法渐近等价。为了选择协变量，我们使用标准套索程序。此外，为了从所得到的套索型估计中做出有效的推断，使用了交叉验证方法。新建议的有效性在理论上和经验上都是正确的。我们用一个著名的原发性胆汁性肝硬变临床试验数据集广泛地说明了这一过程。
To estimate an overall treatment difference with data from a randomized comparative clinical study, baseline covariates are often utilized to increase the estimation precision. Using the standard analysis of covariance technique for making inferences about such an average treatment difference may not be appropriate, especially when the fitted model is nonlinear. On the other hand, the novel augmentation procedure recently studied, for example, by Zhang and others (2008. Improving efficiency of inferences in randomized clinical trials using auxiliary covariates. Biometrics 64, 707-715) is quite flexible. However, in general, it is not clear how to select covariates for augmentation effectively. An overly adjusted estimator may inflate the variance and in some cases be biased. Furthermore, the results from the standard inference procedure by ignoring the sampling variation from the variable selection process may not be valid. In this paper, we first propose an estimation procedure, which augments the simple treatment contrast estimator directly with covariates. The new proposal is asymptotically equivalent to the aforementioned augmentation method. To select covariates, we utilize the standard lasso procedure. Furthermore, to make valid inference from the resulting lasso-type estimator, a cross validation method is used. The validity of the new proposal is justified theoretically and empirically. We illustrate the procedure extensively with a well-known primary biliary cirrhosis clinical trial data set.