TwoPhaseInd: an R package for estimating gene–treatment interactions and discovering predictive markers in randomized clinical trials

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.