Estimating a positive false discovery rate for variable selection in pharmacogenetic studies.
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.
Li
Lang
L
Department of Medicine, Division of Biostatistics, Indiana University, Indianapolis, Indiana 46202, USA. lali@inpui.edu
Hui
Siu
S
Pennello
Gene
G
Desta
Zeruesenay
Z
Todd
Skaar
S
Nguyen
Anne
A
Flockhart
David
D
D000465:Algorithms; D018931:Antineoplastic Agents, Hormonal / Q000493:pharmacokinetics / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy; D003198:Computer Simulation; D003577:Cytochrome P-450 Enzyme System / Q000235:genetics; D003627:Data Interpretation, Statistical; D004965:Estrogen Antagonists / Q000493:pharmacokinetics / Q000627:therapeutic use; D005189:False Positive Reactions; D005260:Female; D005544:Forecasting; D005838:Genotype; D006801:Humans; D016014:Linear Models; D009010:Monte Carlo Method; D010597:Pharmacogenetics / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012107:Research Design; D018401:Sample Size; D013629:Tamoxifen / Q000493:pharmacokinetics / Q000627:therapeutic use
D018931:Antineoplastic Agents, Hormonal; D004965:Estrogen Antagonists; D013629:Tamoxifen; D003577:Cytochrome P-450 Enzyme System