An Even Clearer Portrait of Bias in Observational Studies?
An Even Clearer Portrait of Bias in Observational Studies?
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DOI：
10.1097/ede.0000000000000302
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发表时间：
2015-07-01
期刊：
影响因子：
5.4
通讯作者：
Davies, Neil M.
中科院分区：
文献类型：
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作者：
Davies, Neil M.
尽管这种方法相对简单，但在文献中很少使用。此外，杰克逊和Swanson 3注意到患病率差异比率的一些局限性-他们只提供了关于相对偏倚的信息，而没有提供关于绝对偏倚的信息。这意味着，如果工具变量和OLS偏差都很小，研究人员仍然可以发现一个非常大的患病率差异比率，如果工具变量偏差很小，但略大于OLS偏差。杰克逊和Swanson 3建议以图形形式呈现偏差分量。对于工具变量和OLS估计量，混杂因素对结果α2的影响是相同的，因此两种方法之间的任何偏倚差异都必须归因于偏倚方程中右侧项之间的差异。因此，比较一下就足够了：
Despite this approach’s relative simplicity, it has been rarely used in the literature. Furthermore, Jackson and Swanson3 note some limitations to prevalence difference ratios—they only provide information about relative bias and provide no information about the absolute bias. this means that if the instrumental variable and OLS biases are both very small a researcher could still find a very large prevalence difference ratio if the instrumental variable bias is small, but slightly larger than the OLS bias.Jackson and Swanson3 suggest presenting the bias components in graphical form. the effect of the confounder on the outcome, α2, is the same for both the instrumental variable and OLS estimators, so any difference in bias between the two approaches must be due to the difference between the right hand terms in the bias equations. Therefore, it is sufficient to compare: