A1083
Title: Controlling the false discovery proportion in observational studies with hidden bias
Authors: Colin Fogarty - University of Michigan (United States) [presenting]
Mengqi Lin - University of Michigan (United States)
Abstract: An approach to exploratory data analysis is proposed in matched observational studies. The setting where a single intervention is thought to potentially impact multiple outcome variables is considered, and the aim is to investigate which of these causal hypotheses come to bear while accounting not only for the possibility of false discoveries but also the possibility that the study is plagued by unmeasured confounding. For any candidate set of rejected hypotheses, the method provides sensitivity intervals for the false discovery proportion (FDP), the proportion of rejected hypotheses that are actually true. For a set containing L outcomes, the method describes how much unmeasured confounding would need to exist for us to believe that the proportion of true hypotheses is 0/L,1/L,..., all the way to L/L. Moreover, the resulting confidence statement intervals are valid simultaneously over all possible choices for the rejected set, allowing the researcher to look in an ad hoc manner for promising subsets of outcomes that maintain a large estimated fraction of correct discoveries even if a large degree of unmeasured confounding is present. The approach is particularly well suited to sensitivity analysis, as conclusions that some fraction of outcomes were affected by the treatment exhibit larger robustness to unmeasured confounding than the conclusion that any particular outcome was affected.
