  Evidence from observational studies has become increasingly important for
supporting healthcare policy making via cost-effectiveness (CE) analyses.
Similar as in comparative effectiveness studies, health economic evaluations
that consider subject-level heterogeneity produce individualized treatment
rules (ITRs) that are often more cost-effective than one-size-fits-all
treatment. Thus, it is of great interest to develop statistical tools for
learning such a cost-effective ITR (CE-ITR) under the causal inference
framework that allows proper handling of potential confounding and can be
applied to both trials and observational studies. In this paper, we use the
concept of net-monetary-benefit (NMB) to assess the trade-off between health
benefits and related costs. We estimate CE-ITR as a function of patients'
characteristics that, when implemented, optimizes the allocation of limited
healthcare resources by maximizing health gains while minimizing
treatment-related costs. We employ the conditional random forest approach and
identify the optimal CE-ITR using NMB-based classification algorithms, where
two partitioned estimators are proposed for the subject-specific weights to
effectively incorporate information from censored individuals. We conduct
simulation studies to evaluate the performance of our proposals. We apply our
top-performing algorithm to the NIH-funded Systolic Blood Pressure Intervention
Trial (SPRINT) to illustrate the CE gains of assigning customized intensive
blood pressure therapy.
