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An adaptive Mantel-Haenszel test for sensitivity analysis in observational studies
Paul R Rosenbaum1, Dylan S Small1
1Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.
Biometrics
|October 6, 2016
Summary
For observational studies, a new adaptive test balances using all data versus focusing on subgroups for sensitivity analysis. This method improves power by considering both effect sizes and sample sizes.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Sensitivity analysis is crucial in observational studies with binary outcomes.
- Deciding between using all data or specific subgroups for analysis involves a trade-off between unknown effect sizes and known sample sizes.
Purpose of the Study:
- To propose a novel sensitivity analysis method for observational studies.
- To introduce an adaptive test that performs both focused and combined analyses.
Main Methods:
- Developed an adaptive test analogous to the Mantel-Haenszel test.
- The test conducts two correlated analyses: one on a subgroup and one on the entire dataset.
- Employs joint distribution of test statistics for multiple testing correction, yielding smaller corrections than Bonferroni inequality.
Main Results:
- The adaptive test achieves maximum design sensitivity.
- Simulations demonstrate the enhanced power of this sensitivity analysis approach.
- The procedure is implemented in the R package sensitivity2x2xk.
Conclusions:
- The proposed adaptive test provides a powerful tool for sensitivity analysis in observational studies.
- This method effectively navigates the complexities of effect size and sample size trade-offs.
- The availability of the R package facilitates the application of this statistical technique.
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