Estimating Individualized Treatment Regimes to Optimize Incremental Cost-Effectiveness Ratio

Medical decision making can be challenging due to the trade-off between improving clinical efficacy and the associated medical costs. Evaluation of the incremental cost-effectiveness ratio (ICER) of different treatment programs is important for cost-effectiveness analysis. Individualized treatment regimes (ITRs) that consider patient heterogeneity can lead to varying health benefits and costs. To identify a promising ITR that balances efficacy and cost, the ICER criterion can be used to evaluate the quality of the ITR. We propose a method that considers both health benefits and costs to derive the the optimal ITR. We utilize Dinkelbach’s algorithm to transform a fractional program into a parametric program, which is easier to handle. We compare our method to ITRs that only optimize a single outcome (benefits or costs) through extensive simulation studies and show that our approach performs satisfactorily. To demonstrate the practical application of our method, we apply it to the Multicenter Automatic Defibrillator Implantation Trial with Cardiac Resynchronization Therapy (MADIT-CRT) study, a randomized trial. Our approach can help identify an optimal ITR that balances the trade-off between clinical efficacy and medical costs. Overall, our method provides a valuable tool for medical decision making that takes into account patient heterogeneity and cost-effectiveness analysis.