A false-discovery-rate-based loss framework for selection of interactions

Archived Abstract of Former PSC Researcher

Chen, W., D. Ghosh, Trivellore Raghunathan, and D.J. Sargent. 2008. "A false-discovery-rate-based loss framework for selection of interactions." Statistics in Medicine, 27(11): 2004-2021.

Interaction effects have been consistently found important in explaining the variation in outcomes in many scientific research fields. Yet, in practice, variable selection including interactions is complicated due to the limited sample size, conflicting philosophies regarding model interpretability, and accompanying amplified multiple-testing problems. The lack of statistically sound algorithms for automatic variable selection with interactions has discouraged activities in exploring important interaction effects. In this article, we investigated issues of selecting interactions from three aspects: (1) What is the model space to be searched? (2) How is the hypothesis-testing performed? (3) How to address the multiple-testing issue? We propose loss functions and corresponding decision rules that control FDR in a Bayesian context. Properties of the decision rules are discussed and their performance in terms of power and FDR is compared through simulations. Methods are illustrated on data from a colorectal cancer study assessing the chemotherapy treatments and data from a diffuse large-B-cell lymphoma study assessing the prognostic effect of gene expressions. Copyright (c) 2007 John Wiley & Sons, Ltd.

10.1002/sim.3118

Browse | Search | Next

PSC In The News

RSS Feed icon

Shaefer comments on the Cares Act impact in negating hardship during COVID-19 pandemic

Heller comments on lasting safety benefit of youth employment programs

More News

Highlights

Dean Yang's Combatting COVID-19 in Mozambique study releases Round 1 summary report

Help Establish Standard Data Collection Protocols for COVID-19 Research

More Highlights


Connect with PSC follow PSC on Twitter Like PSC on Facebook