Basic methods for sensitivity analysis of biases
Basic methods for sensitivity analysis of biases
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DOI：
10.1093/ije/25.6.1107-a
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发表时间：
1996-12-01
影响因子：
7.7
通讯作者：
Greenland, S
中科院分区：
文献类型：
--
作者：
Greenland, S
背景大多数关于统计方法的讨论都集中在解释数据生成过程中的测量混杂因素和随机误差。然而，在观察流行病学中，可控混杂和随机误差有时只是总误差的一小部分，而且很少是不确定性的唯一重要来源。由于未测量的混杂因素、分类错误和选择偏倚导致的潜在偏倚需要在对研究结果的任何彻底讨论中加以解决。本文综述了研究结果对偏倚敏感性的基本方法，重点介绍了无需计算机编程即可实现的方法。敏感性分析有助于获得偏见潜在影响的现实情况。
Background. Most discussions of statistical methods focus on accounting for measured confounders and random errors in the data-generating process. In observational epidemiology, however, controllable confounding and random error are sometimes only a fraction of the total error, and are rarely if ever the only important source of uncertainty. Potential biases due to unmeasured confounders, classification errors, and selection bias need to be addressed in any thorough discussion of study results.Methods. This paper reviews basic methods for examining the sensitivity of study results to biases, with a focus on methods that can be implemented without computer programming.Conclusion. Sensitivity analysis is helpful in obtaining a realistic picture of the potential impact of biases.