Findings from TrialTranslator, a machine-learning model involving randomized clinical trials (RCTs), indicates that "median overall survival treatment benefit for real-word patients...[is] on average, 3 months lower than in RCTs..." While the authors of this TrialTranslator study conclude that "prognostic heterogeneity among real-world patients with cancer plays a substantial role in the limited generalizability of RCT results", machine learning frameworks have been shown to be of benefit in guiding trial design and planning future enhancements in cancer care.
To read more about TrialTranslator, click here.
Source mentioned:
Orcutt X, Chen K, Mamtani R, et al. Evaluating generalizability of oncology trial results to real-world patients using machine learning-based trial emulations. Nature Medicine; Published online 3 January 2025.
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