Sensitivity Analysis Without Assumptions

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Sensitivity Analysis Without Assumptions

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Title: Sensitivity Analysis Without Assumptions
Author: Ding, Peng; VanderWeele, Tyler J.

Note: Order does not necessarily reflect citation order of authors.

Citation: Ding, Peng, and Tyler J. VanderWeele. 2016. “Sensitivity Analysis Without Assumptions.” Epidemiology (Cambridge, Mass.) 27 (3): 368-377. doi:10.1097/EDE.0000000000000457. http://dx.doi.org/10.1097/EDE.0000000000000457.
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Abstract: 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.
Published Version: doi:10.1097/EDE.0000000000000457
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4820664/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:26860180
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