title: Using local multiplicity to improve effect estimation from a hypothesis-generating pharmacogenetics study.
abstract: We propose a multiple estimation adjustment (MEA) method to correct effect overestimation due to selection bias from a hypothesis-generating study (HGS) in pharmacogenetics. MEA uses a hierarchical Bayesian approach to model individual effect estimates from maximal likelihood estimation (MLE) in a region jointly and shrinks them toward the regional effect. Unlike many methods that model a fixed selection scheme, MEA capitalizes on local multiplicity independent of selection. We compared mean square errors (MSEs) in simulated HGSs from naive MLE, MEA and a conditional likelihood adjustment (CLA) method that model threshold selection bias. We observed that MEA effectively reduced MSE from MLE on null effects with or without selection, and had a clear advantage over CLA on extreme MLE estimates from null effects under lenient threshold selection in small samples, which are common among 'top' associations from a pharmacogenetics HGS.
authors: Biostatistics, Genentech, Inc., 1 DNA Way, South San Francisco, CA, USA.
authors: W
authors: W
authors: Zou
authors: Global Statistical Sciences (GSS) - Oncology, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN, USA.
authors: H
authors: H
authors: Ouyang
mesh_terms: D001499:Bayes Theorem; D056726:Genetic Association Studies / Q000379:methods* / Q000706:statistics & numerical data; D016013:Likelihood Functions; D010597:Pharmacogenetics / Q000379:methods* / Q000706:statistics & numerical data; D020641:Polymorphism, Single Nucleotide