  Medical treatments tailored to a patient's baseline characteristics hold the
potential of improving patient outcomes while reducing negative side effects.
Learning individualized treatment rules (ITRs) often requires aggregation of
multiple datasets(sites); however, current ITR methodology does not take
between-site heterogeneity into account, which can hurt model generalizability
when deploying back to each site. To address this problem, we develop a method
for individual-level meta-analysis of ITRs, which jointly learns site-specific
ITRs while borrowing information about feature sign-coherency via a
scientifically-motivated directionality principle. We also develop an adaptive
procedure for model tuning, using information criteria tailored to the ITR
learning problem. We study the proposed methods through numerical experiments
to understand their performance under different levels of between-site
heterogeneity and apply the methodology to estimate ITRs in a large
multi-center database of electronic health records. This work extends several
popular methodologies for estimating ITRs (A-learning, weighted learning) to
the multiple-sites setting.
