An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest
利用条件随机森林高效优化成本有效的个体化治疗规则
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…