An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest

TL;DR

This paper introduces a novel method using conditional random forests to optimize individualized treatment rules for cost-effectiveness in healthcare, effectively handling confounding and censored data to improve resource allocation.

Contribution

It develops a new CE-ITR estimation approach based on NMB and conditional random forests, incorporating censored data handling, for better personalized healthcare decisions.

Findings

The proposed method outperforms existing approaches in simulations.

Application to SPRINT data demonstrates significant CE gains with personalized treatment.

Effective handling of censored data improves treatment rule accuracy.

Abstract

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.