Yu Binbing, Gastwirth Joseph L
Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, Bethesda, MD 20892, USA.
Philos Trans A Math Phys Eng Sci. 2008 Jul 13;366(1874):2377-88. doi: 10.1098/rsta.2008.0030.
Observational studies, including the case-control design frequently used in epidemiology, are subject to a number of biases and possible confounding factors. Failure to adjust with them may lead to an erroneous conclusion about the existence of a causal relationship between exposure and disease. The Cochran-Mantel-Haenszel (CMH) test is widely used to measure the strength of the association between an exposure and disease or response, after stratifying on the observed covariates. Thus, observed confounders are accounted for in the analysis. In practice, there may be causal variables that are unknown or difficult to obtain. Hence, they are not incorporated into the analysis. Sensitivity analysis enables investigators to assess the robustness of the findings. A method for assessing the sensitivity of the CMH test to an omitted confounder is presented here. The technique is illustrated by re-examining two datasets: one concerns the effect of maternal hypertension as a risk factor for low birth weight infants and the other focuses on the risk of allopurinol on having a rash. The computer code performing the sensitivity analysis is provided in appendix A.
观察性研究，包括流行病学中常用的病例对照设计，容易受到多种偏差和潜在混杂因素的影响。未能对这些因素进行调整可能会导致关于暴露与疾病之间因果关系存在与否的错误结论。 Cochr an-Mantel-Haenszel（CMH）检验在对观察到的协变量进行分层后，被广泛用于衡量暴露与疾病或反应之间关联的强度。因此，在分析中会考虑观察到的混杂因素。在实际中，可能存在一些未知或难以获取的因果变量。因此，它们未被纳入分析。敏感性分析使研究人员能够评估研究结果的稳健性。本文介绍了一种评估CMH检验对遗漏混杂因素敏感性的方法。通过重新审视两个数据集来说明该技术：一个涉及孕妇高血压作为低体重儿风险因素的影响，另一个关注别嘌醇引发皮疹的风险。附录A中提供了执行敏感性分析的计算机代码。
