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Using local multiplicity to improve effect estimation from a hypothesis-generating pharmacogenetics study

The Pharmacogenomics Journal volumeΒ 16,Β pages 107–112 (2016)Cite this article

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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 β€˜top’ associations from a pharmacogenetics HGS.

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Acknowledgements

We thank Bonnie Fijal for helpful discussions and comments and anonymous reviewers for their comments.

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Authors and Affiliations

  1. Biostatistics, Genentech, Inc., 1 DNA Way, South San Francisco, CA, USA

    W Zou

  2. Global Statistical Sciences (GSS) – Oncology, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN, USA

    H Ouyang

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  1. W Zou
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  2. H Ouyang
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Correspondence to H Ouyang.

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Competing interests

WZ completed most of the work as an employee of InVentiv Health Clinical, LLC and HO is an employee of Eli Lilly and Company.

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Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website

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Zou, W., Ouyang, H. Using local multiplicity to improve effect estimation from a hypothesis-generating pharmacogenetics study. Pharmacogenomics J 16, 107–112 (2016). https://doi.org/10.1038/tpj.2015.19

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