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Evaluating eligibility criteria of oncology trials using real-world data and AI
Open accessAbstract
There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging1–3. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes with real-world data using the computational framework of Trial Pathfinder. We apply Trial Pathfinder to emulate completed trials of advanced non-small-cell lung cancer using data from a nationwide database of electronic health records comprising 61,094 patients with advanced non-small-cell lung cancer. Our analyses reveal that many common criteria, including exclusions based on several laboratory values, had a minimal effect on the trial hazard ratios. When we used a data-driven approach to broaden restrictive criteria, the pool of eligible patients more than doubled on average and the hazard ratio of the overall survival decreased by an average of 0.05. This suggests that many patients who were not eligible under the original trial criteria could potentially benefit from the treatments. We further support our findings through analyses of other types of cancer and patient-safety data from diverse clinical trials. Our data-driven methodology for evaluating eligibility criteria can facilitate the design of more-inclusive trials while maintaining safeguards for patient safety. Trial Pathfinder uses data from electronic health records of patients with cancer to evaluate eligibility criteria and broaden restrictive criteria, which facilitates the design of more-inclusive trials while maintaining safeguards for patient safety.
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A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
Advances in neural information processing systems 7
Cios, K., & Shields, M. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,059
Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer
Borghaei, H., Paz-Ares, L., Horn, L., Spigel, D. R., Steins, M., Ready, N. E., Chow, L. Q., Vokes, E. E., Felip, E., Holgado, E., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1507643 · Cited by 9,335
Nivolumab versus Docetaxel in Advanced Squamous-Cell Non–Small-Cell Lung Cancer
Brahmer, J., Reckamp, K. L., Baas, P., Crinò, L., Eberhardt, W. E. E., Poddubskaya, E., Antonia, S., Pluzanski, A., Vokes, E. E., Holgado, E., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1504627 · Cited by 8,447
Pembrolizumab plus chemotherapy in metastatic non–small-cell lung cancer
Gandhi, L., Rodríguez-Abreu, D., Gadgeel, S., Esteban, E., Felip, E., De Angelis, F., Domine, M., Clingan, P., Hochmair, M. J., Powell, S. F., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1801005 · Cited by 6,607
Herbst, R. S., Baas, P., Kim, D.-W., Felip, E., Pérez-Gracia, J. L., Han, J.-Y., Molina, J., Kim, J.-H., Arvis, C. D., Ahn, M.-J., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(15)01281-7 · Cited by 6,449
Osimertinib in Untreated EGFR -Mutated Advanced Non–Small-Cell Lung Cancer
Soria, J.-C., Ohe, Y., Vansteenkiste, J., Reungwetwattana, T., Chewaskulyong, B., Lee, K. H., Dechaphunkul, A., Imamura, F., Nogami, N., Kurata, T., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1713137 · Cited by 5,508
Rittmeyer, A., Barlesi, F., Waterkamp, D., Park, K., Ciardiello, F., von Pawel, J., Gadgeel, S. M., Hida, T., Kowalski, D. M., Dols, M. C., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(16)32517-x · Cited by 4,675
Pembrolizumab plus chemotherapy for squamous non-small-cell lung cancer
Paz-Ares, L., Luft, A., Vicente, D., Tafreshi, A., Gümüş, M., Mazières, J., Hermes, B., Çay Şenler, F., Csőszi, T., Fülöp, A., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1810865 · Cited by 3,617
Frampton, G. M., Fichtenholtz, A., Otto, G. A., Wang, K., Downing, S. R., He, J., Schnall-Levin, M., White, J., Sanford, E. M., An, P., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2696 · Cited by 2,157
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Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254
Brauner, P., Hick, A., Philipsen, R., & Ziefle, M. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1113903 · Cited by 143
Moon, I., LoPiccolo, J., Baca, S. C., Sholl, L. M., Kehl, K. L., Hassett, M. J., Liu, D., Schrag, D., & Gusev, A. (2023) · Nature Medicine · doi:10.1038/s41591-023-02482-6 · Cited by 122
Azoulay, L. (2022) · The Oncologist · doi:10.1093/oncolo/oyac114 · Cited by 79
Metadata
- Authors
- Liu, R., Rizzo, S., Whipple, S., Pal, N., Pineda, A. L., Lu, M., Arnieri, B., Lu, Y., Capra, W., Copping, R., & Zou, J.
- Venue
- Nature
- Published
- 2021-04-07
- Volume
- 592
- Issue
- 7855
- Pages
- 629-633
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3140760072
- Cited by
- 322
- References
- 41
- FWCI
- 34.21
- Type
- journal article
- Language
- en
- Open access
- bronze
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