Estimating a positive false discovery rate for variable selection in pharmacogenetic studies

Lang Li, Siu Hui, Gene Pennello, Zeruesenay Desta, Skaar Todd, Anne Nguyen, David Flockhart

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Selecting predictors to optimize the outcome prediction is an important statistical method. However, it usually ignores the false positives in the selected predictors. In this paper, we develop a positive false discovery rate (pFDR) estimate for a conventional step-wise forward variable selection procedure. We propose two views of a variable selection process, an overall and an individual test. An interesting feature of the overall test is that its power of selecting non-null predictors increases with the proportion of non-null predictors among all candidate predictors. Data analysis is illustrated with a pharmacogenetics example.

Original languageEnglish (US)
Pages (from-to)883-902
Number of pages20
JournalJournal of biopharmaceutical statistics
Volume17
Issue number5
DOIs
StatePublished - Sep 1 2007

Keywords

  • Cross-validation
  • False discovery rate
  • Multiple-comparisons
  • Pharmacogenetics
  • Variable selection

ASJC Scopus subject areas

  • Pharmacology (medical)
  • Pharmacology, Toxicology and Pharmaceutics(all)

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