Open Access
2018 False discovery rate control for effect modification in observational studies
Bikram Karmakar, Ruth Heller, Dylan S. Small
Electron. J. Statist. 12(2): 3232-3253 (2018). DOI: 10.1214/18-EJS1476


In an observational study, a difference between the treatment and control group’s outcome might reflect the bias in treatment assignment rather than a true treatment effect. A sensitivity analysis determines the magnitude of this bias that would be needed to explain away as non-causal a significant treatment effect from a naive analysis that assumed no bias. Effect modification is the interaction between a treatment and a pretreatment covariate. In an observational study, there are often many possible effect modifiers and it is desirable to be able to look at the data to identify the effect modifiers that will be tested. For observational studies, we address simultaneously the problem of accounting for the multiplicity involved in choosing effect modifiers to test among many possible effect modifiers by looking at the data and conducting a proper sensitivity analysis. We develop an approach that provides finite sample false discovery rate control for a collection of adaptive hypotheses identified from the data on matched-pairs design. Along with simulation studies, an empirical study is presented on the effect of cigarette smoking on lead level in the blood using data from the U.S. National Health and Nutrition Examination Survey. Other applications of the suggested method are briefly discussed.


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Bikram Karmakar. Ruth Heller. Dylan S. Small. "False discovery rate control for effect modification in observational studies." Electron. J. Statist. 12 (2) 3232 - 3253, 2018.


Received: 1 May 2017; Published: 2018
First available in Project Euclid: 2 October 2018

zbMATH: 06970003
MathSciNet: MR3860111
Digital Object Identifier: 10.1214/18-EJS1476

Keywords: classification and regression trees , effect modification , sensitivity analysis , simultaneous testing , treatment effect

Vol.12 • No. 2 • 2018
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