ArticleNature medicine2025
Evaluating generalizability of oncology trial results to real-world patients using machine learning-based trial emulations.
Article in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- AI-Enabled Real-World Evidence in Oncology: A Statistical Perspective for Regulatory Decisions.Therapeutic innovation & regulatory science · 2026Review
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- Empowering clinical trial design with agentic intelligence and real-world data.Nature communications · 2026Article
- Understanding AI risks from its characteristics and NMPA regulation perspectives.Insights into imaging · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Embracing the Digital Revolution: How Artificial Intelligence is Transforming Clinical Trials in Older Participants.Drugs & aging · 2026Review
- Mapping eligibility criteria in oncology target trial emulations using real-world data: a scoping review.BMC medical research methodology · 2026Article
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Article
- Real-World Genomic Landscape of Korean Gastric Cancer: Integrating Biomarker Associations and Clinical Outcomes in Metastatic Gastric Cancer.JCO precision oncology · 2026Article
- Major Advances in Gynecologic Oncology in 2025: Systematic Review and Synthesis of Conference and Published Evidence.Biomedicines · 2026Review
- Integrating chemokine signatures and multi-omic biomarkers to predict immunotherapy response in non-small cell lung cancer: a comprehensive narrative review.Frontiers in oncology · 2026Review
- Peripheral blood biomarkers in PD-1/PD-L1 immunotherapy: distinguishing predictive from prognostic biomarkers.Frontiers in immunology · 2026Review
- The Five-Decade Journey of Small Cell Lung Cancer.Cancer communications (London, England) · 2026Review
- The impact of eligibility criteria on Kirsten rat sarcoma G12C inhibitor trials in patients with non-small cell lung cancer.Journal of the National Cancer Institute · 2026Article
- Long-term comparative effectiveness of tepotinib versus standard of care for MET exon 14 skipping-mutated non-small cell lung cancer.Therapeutic advances in medical oncology · 2026Article
- Health Disparities in Acute Myeloid Leukemia Patients Undergoing Treatment with Tyrosine Kinase Inhibitor (TKI) Therapy Targeting FLT3, IDH1, or IDH2.Blood and lymphatic cancer : targets and therapy · 2026Article
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
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 patients in low-risk and medium-risk phenotypes exhibit survival times and treatment-associated survival benefits similar to those observed in RCTs. In contrast, high-risk phenotypes show significantly lower survival times and treatment-associated survival benefits compared to RCTs. Our results were corroborated by a comprehensive robustness assessment, including examinations of specific patient subgroups, holdout validation and semi-synthetic data simulation. These findings suggest that the prognostic heterogeneity among real-world oncology patients plays a substantial role in the limited generalizability of RCT results. Machine learning frameworks may facilitate individual patient-level decision support and estimation of real-world treatment benefits to guide trial design.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.