On sensitivity value of pair-matched observational studies

Abstract

An observational study may be biased for estimating causal effects by failing to control for unmeasured confounders. This paper proposes a new quantity called the "sensitivity value", which is defined as the minimum strength of unmeasured confounders needed to change the qualitative conclusions of a naive analysis assuming no unmeasured confounder. We establish the asymptotic normality of the sensitivity value in pair-matched observational studies. The theoretical results are then used to approximate the power of a sensitivity analysis and select the design of a study. We explore the potential to use sensitivity values to screen multiple hypotheses in presence of unmeasured confounding using a microarray dataset.

17 pages, 5 figures, 4 tables

References in corpus (1)

• Cross-screening in observational studies that test many hypotheses

Cited by in corpus (1)

• Multiple testing when many $p$-values are uniformly conservative, with application to testing qualitative interaction in educational interventions