Statistics and Data Science Seminar
Rui Song
North Carolina State University
Concordance-Assisted Learning for Individualized Treatment Regimes
Abstract: In the first part of the talk, we propose a new concordance-assisted
learning for estimating optimal individualized treatment regimes. We
first introduce a type of concordance function for prescribing
treatment and propose a robust rank regression method for estimating
the concordance function. We then find treatment regimes, up to a
threshold, to maximize the concordance function, named prescriptive
index. Finally, within the class of treatment regimes that maximize
the concordance function, we find the optimal threshold to maximize
the value function. Although this method makes better use of the
available information through pairwise comparison, the objective
function is discontinuous and computationally hard to optimize. In the
second part of the talk, we consider a convex surrogate loss function
to solve this problem. In addition, our algorithm ensures sparsity of
decision rule and makes it easy to interpret. Simulation results of
various settings and application to STAR*D both illustrate that the
proposed method can still estimate optimal treatment regime
successfully when the numb of covariates is large.
Wednesday April 25, 2018 at 4:00 PM in SEO 636