The R.O.A.D. to precision medicine.

Autor: Bertsimas D; Sloan School of Management and Operations Research Center, E62-560, Massachusetts Institute of Technology, Boston, MA, USA., Koulouras AG; Sloan School of Management and Operations Research Center, E62-560, Massachusetts Institute of Technology, Boston, MA, USA., Margonis GA; Sloan School of Management and Operations Research Center, E62-560, Massachusetts Institute of Technology, Boston, MA, USA. margonig@mskcc.org.; Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA. margonig@mskcc.org.
Jazyk: angličtina
Zdroj: NPJ digital medicine [NPJ Digit Med] 2024 Nov 03; Vol. 7 (1), pp. 307. Date of Electronic Publication: 2024 Nov 03.
DOI: 10.1038/s41746-024-01291-6
Abstrakt: We propose a novel framework that addresses the deficiencies of Randomized clinical trial data subgroup analysis while it transforms ObservAtional Data to be used as if they were randomized, thus paving the road for precision medicine. Our approach counters the effects of unobserved confounding in observational data through a two-step process that adjusts predicted outcomes under treatment. These adjusted predictions train decision trees, optimizing treatment assignments for patient subgroups based on their characteristics, enabling intuitive treatment recommendations. Implementing this framework on gastrointestinal stromal tumors (GIST) data, including genetic sub-cohorts, showed that our tree recommendations outperformed current guidelines in an external cohort. Furthermore, we extended the application of this framework to RCT data from patients with extremity sarcomas. Despite initial trial indications of universal treatment necessity, our framework identified a subset of patients who may not require treatment. Once again, we successfully validated our recommendations in an external cohort.
(© 2024. The Author(s).)
Databáze: MEDLINE
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