title: TwoPhaseInd: an R package for estimating gene-treatment interactions and discovering predictive markers in randomized clinical trials.
abstract: In randomized clinical trials, identifying baseline genetic or genomic markers for predicting subgroup treatment effects is of rising interest. Outcome-dependent sampling is often employed for measuring markers. The R package TwoPhaseInd implements a number of efficient statistical methods we developed for estimating subgroup treatment effects and gene-treatment interactions, exploiting the gene-treatment independence dictated by randomization, including the case-only estimator, the maximum estimated likelihood estimator and the semiparametric maximum likelihood estimator for parameters in a logistic model. For rare failure events subject to censoring, we have proposed efficient augmented case-only designs, a variation of the case-cohort design, to estimate genetic associations and subgroup treatment effects in a Cox regression model. The R package is computationally scalable to genome-wide studies, as illustrated by an example from Women's Health Initiative.
  
  
  
  The R package TwoPhaseInd is available from http://cran.r-project.org/web/packages CONTACT: jdai@fredhutch.org.
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Wang
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: James Y
authors.initials: JY
authors.lastname: Dai
mesh_terms: D015415:Biomarkers; D016678:Genome; D006801:Humans; D016015:Logistic Models; D016032:Randomized Controlled Trials as Topic; D012984:Software
chemical_list: D015415:Biomarkers