One central goal of design of observational studies is to embed non-experimental data into an approximate randomized controlled trial using statistical matching. Despite empirical researchers' best intention and effort to create high-quality matched samples, residual imbalance due to observed covariates not being well matched often persists. Although statistical tests have been developed to test the randomization assumption and its implications, few provide a means to quantify the level of residual confounding due to observed covariates not being well matched in matched samples. In this article, we develop two generic classes of exact statistical tests for a biased randomization assumption. One important by-product of our testing framework is a quantity called residual sensitivity value (RSV), which provides a means to quantify the level of residual confounding due to imperfect matching of observed covariates in a matched sample. We advocate taking into account RSV in the downstream primary analysis. The proposed methodology is illustrated by re-examining a famous observational study concerning the effect of right heart catheterization (RHC) in the initial care of critically ill patients. Code implementing the method can be found in the supplementary materials.
翻译:设计观测研究的一个中心目标是利用统计匹配,将非实验性数据纳入一个近似随机控制的试验中。尽管经验研究人员最愿意并努力建立高质量的匹配样本,但由于观察到的共变体不完全匹配而导致的剩余不平衡现象往往继续存在。虽然已经开发了统计测试来测试随机化假设及其影响,但几乎没有什么方法可以量化观察到的共变体在匹配样本中不匹配造成的剩余混杂程度。在本条中,我们为偏差随机化假设制定了两种类型的精确统计测试的通用类别。我们测试框架的一个重要副产品是称为残余敏感值(RSV)的数量,它提供了一种量化因在匹配样本中观察到的共变体不完全匹配而导致的残余混杂程度的手段。我们主张在下游初级分析中考虑到共变体的残留情况。我们建议的方法是通过重新研究关于右心导化(RHC)对重病病人初步护理的影响的著名观察研究。在补充材料中可以找到实施该方法的守则。