Commentary: Bias Attenuation and Identi fi cation of Causal Effects With Multiple Negative Controls
Commentary: Bias Attenuation and Identi fi cation of Causal Effects With Multiple Negative Controls
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发表时间：
2017
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通讯作者：
Wang Miao;E. T. Tchetgen
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作者：
Wang Miao;E. T. Tchetgen
在这篇评论中，我们描述了由Flanders等人提出的用于时间序列分析中部分混杂调整的有趣而重要的负对照暴露方法的几个扩展。(Am J Epidinool.)2017；185(10)：941-949)。具体地说，通过利用暴露时间序列的可用性，我们表明，在某些额外的相当合理的假设下，可以将过去和未来的暴露合并为多个负对照暴露，以进一步减弱混杂偏差。我们进一步描述了两种特定的fic设置，其中可以使用多个控制来完全解释混杂偏差；first假设曝光时间序列的时间版本为熟悉的自回归模型，而第二种设置将负控制暴露与负控制结果相结合，用于联合间接调整混杂。我们简要说明了如何将我们提出的框架应用于时间序列研究。fly。这两种方法都是弗兰德斯等人提出的。我们建议的延期特别适合于时间序列数据，例如他们的论文中考虑的空气污染研究，因此应该在常规的环境健康研究中考虑。
In this commentary, we describe several extensions to the interesting and important negative control exposure approach for partial confounding adjustment in time-series analysis proposed by Flanders et al. ( Am J Epidemiol . 2017;185(10):941 – 949). Speci fi cally, by leveraging the availability of exposure time series, we show that under certain additional fairly reasonable assumptions, one can incorporate both past and future exposures as multiple negative control exposures to further attenuate confounding bias. We further describe 2 speci fi c settings in which multiple controls can be used to fully account for confounding bias; the fi rst assumes a forward-in-time version of the familiar autoregressive model for the exposure time series, while the second combines a negative control exposure with a negative control outcome for joint indirect adjustment of confounding. We brie fl y illustrate how one might apply our proposed framework in time-series studies. Both the original method of Flanders et al. and our proposed extensions are particularly well-suited for time-series data such as the air pollution study considered in their paper, and as such should be considered in routine environmental health studies.