  To maximize clinical benefit, clinicians routinely tailor treatment to the
individual characteristics of each patient, where individualized treatment
rules are needed and are of significant research interest to statisticians. In
the covariate-adjusted randomization clinical trial with many covariates, we
model the treatment effect with an unspecified function of a single index of
the covariates and leave the baseline response completely arbitrary. We devise
a class of estimators to consistently estimate the treatment effect function
and its associated index while bypassing the estimation of the baseline
response, which is subject to the curse of dimensionality. We further develop
inference tools to identify predictive covariates and isolate effective
treatment region. The usefulness of the methods is demonstrated in both
simulations and a clinical data example.
