Estimating Individualized Treatment Regimes to Optimize Incremental Cost-Effectiveness Ratio

Xinyuan Dong () and Ying-Qi Zhao ()
Additional contact information
Xinyuan Dong: Amazon.com, Inc
Ying-Qi Zhao: Fred Hutchinson Cancer Center

Statistics in Biosciences, 2025, vol. 17, issue 2, No 6, 366-385

Abstract: Abstract 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.

Keywords: Cost-effectiveness analysis; Incremental cost-effectiveness ratio; Personalized medicine; Ratio optimization; Machine learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-024-09440-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:17:y:2025:i:2:d:10.1007_s12561-024-09440-x

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-024-09440-x

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().