The projack: a resampling approach to correct for ranking bias in high-throughput studies.

ABSTRACT: The problem of ranked inference arises in a number of settings, for which the investigator wishes to perform parameter inference after ordering a set of [Formula: see text] statistics. In contrast to inference for a single hypothesis, the ranking procedure introduces considerable bias, a problem known as the "winner's curse" in genetic association. We introduce the projack (for Prediction by Re- Ordered Jackknife and Cross-Validation, [Formula: see text]-fold). The projack is a resampling-based procedure that provides low-bias estimates of the expected ranked effect size parameter for a set of possibly correlated [Formula: see text] statistics. The approach is flexible, and has wide applicability to high-dimensional datasets, including those arising from genomics platforms. Initially, moti

SUBMITTER: Zhou YH 

PROVIDER: S-EPMC4679068 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature