Evaluating generalizability of oncology trial results to real-world patients using machine learning-based trial emulations.

ABSTRACT: Randomized controlled trials (RCTs) evaluating anti-cancer agents often lack generalizability to real-world oncology patients. Although restrictive eligibility criteria contribute to this issue, the role of selection bias related to prognostic risk remains unclear. In this study, we developed TrialTranslator, a framework designed to systematically evaluate the generalizability of RCTs for oncology therapies. Using a nationwide database of electronic health records from Flatiron Health, this framework emulates RCTs across three prognostic phenotypes identified through machine learning models. We applied this approach to 11 landmark RCTs that investigated anti-cancer regimens considered standard of care for the four most prevalent advanced solid malignancies. Our analyses reveal that patient

SUBMITTER: Orcutt X 

PROVIDER: S-EPMC11835724 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature