Sensitivity Analysis Without Assumptions.
Sensitivity Analysis Without Assumptions.
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DOI：
10.1097/ede.0000000000000457
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
VanderWeele TJ
中科院分区：
文献类型：
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作者：
Ding P;VanderWeele TJ
补充数字内容可在文本中找到。未测量的混杂可能会破坏观察性研究因果推断的有效性。敏感性分析通过评估未测量混杂对因果结论的潜在影响，提供了一种有吸引力的方法来部分规避这一问题。然而，以前的敏感性分析方法经常做出强烈的和不可检验的假设，例如有一个不可测量的混杂因素是二元的，或者暴露和混杂因素对结果的影响之间没有相互作用，或者只有一个不可测量的混杂因素。在不对未测量的混杂因素施加任何假设的情况下，我们推导出一个边界因子和一个尖锐的不等式，这样，如果一个未测量的混杂因素要解释掉观察到的效应估计或将其降低到一个特定的水平，灵敏度分析参数必须满足不等式。该方法易于实现，且只涉及两个灵敏度参数。令人惊讶的是，我们的边界因子，它没有简化假设，并不比以前一些做假设的敏感性分析技术更保守。我们的新边界因子不仅意味着传统的玉米地条件，即混杂物暴露的相对风险和混杂物对结果的相对风险都必须满足，而且还意味着这些相对风险的最大值必须满足一个高阈值。此外，这个新的边界因子可以被看作是暴露和混杂因素引起的结果之间混杂强度的度量。
Supplemental Digital Content is available in the text. Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on causal conclusions. However, previous sensitivity analysis approaches often make strong and untestable assumptions such as having an unmeasured confounder that is binary, or having no interaction between the effects of the exposure and the confounder on the outcome, or having only one unmeasured confounder. Without imposing any assumptions on the unmeasured confounder or confounders, we derive a bounding factor and a sharp inequality such that the sensitivity analysis parameters must satisfy the inequality if an unmeasured confounder is to explain away the observed effect estimate or reduce it to a particular level. Our approach is easy to implement and involves only two sensitivity parameters. Surprisingly, our bounding factor, which makes no simplifying assumptions, is no more conservative than a number of previous sensitivity analysis techniques that do make assumptions. Our new bounding factor implies not only the traditional Cornfield conditions that both the relative risk of the exposure on the confounder and that of the confounder on the outcome must satisfy but also a high threshold that the maximum of these relative risks must satisfy. Furthermore, this new bounding factor can be viewed as a measure of the strength of confounding between the exposure and the outcome induced by a confounder.