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

Meta-analysis of individualized treatment rules via sign-coherency