title: An adaptive Mantel-Haenszel test for sensitivity analysis in observational studies.
abstract: In a sensitivity analysis in an observational study with a binary outcome, is it better to use all of the data or to focus on subgroups that are expected to experience the largest treatment effects? The answer depends on features of the data that may be difficult to anticipate, a trade-off between unknown effect-sizes and known sample sizes. We propose a sensitivity analysis for an adaptive test similar to the Mantel-Haenszel test. The adaptive test performs two highly correlated analyses, one focused analysis using a subgroup, one combined analysis using all of the data, correcting for multiple testing using the joint distribution of the two test statistics. Because the two component tests are highly correlated, this correction for multiple testing is small compared with, for instance, the Bonferroni inequality. The test has the maximum design sensitivity of two component tests. A simulation evaluates the power of a sensitivity analysis using the adaptive test. Two examples are presented. An R package, sensitivity2x2xk, implements the procedure.
authors.affiliation: Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.
authors.forename: Paul R
authors.initials: PR
authors.lastname: Rosenbaum
authors.affiliation: Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.
authors.forename: Dylan S
authors.initials: DS
authors.lastname: Small
mesh_terms: D001699:Biometry; D006801:Humans; D015233:Models, Statistical; D064887:Observational Studies as Topic; D018401:Sample Size
keywords: Causal inference; Counter-factually low risk cases; Design sensitivity; Observational study; Power of a sensitivity analysis; Sensitivity analysis