The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Real-world Data for Clinical Evidence Generation in Oncology
Open accessAbstract
Conventional cancer clinical trials can be slow and costly, often produce results with limited external validity, and are difficult for patients to participate in. Recent technological advances and a dynamic policy landscape in the United States have created a fertile ground for the use of real-world data (RWD) to improve current methods of clinical evidence generation. Sources of RWD include electronic health records, insurance claims, patient registries, and digital health solutions outside of conventional clinical trials. A definition focused on the original intent of data collected at the point of care can distinguish RWD from conventional clinical trial data. When the intent of data collection at the point of care is research, RWD can be generated using experimental designs similar to those employed in conventional clinical trials, but with several advantages that include gains in efficient execution of studies with an appropriate balance between internal and external validity. RWD can support active pharmacovigilance, insights into the natural history of disease, and the development of external control arms. Prospective collection of RWD can enable evidence generation based on pragmatic clinical trials (PCTs) that support randomized study designs and expand clinical research to the point of care. PCTs may help address the growing demands for access to experimental therapies while increasing patient participation in cancer clinical trials. Conducting valid real-world studies requires data quality assurance through auditable data abstraction methods and new incentives to drive electronic capture of clinically relevant data at the point of care.
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Cited by
ESMO guidance for reporting oncology real-world evidence (GROW)
Castelo-Branco, L., Pellat, A., Martins-Branco, D., Valachis, A., Derksen, J. W. G., Suijkerbuijk, K. P. M., Dafni, U., Dellaporta, T., Vogel, A., Prelaj, A., et al. (2023) · Annals of Oncology · doi:10.1016/j.annonc.2023.10.001 · Cited by 247
Harnessing Real-World Evidence to Advance Cancer Research
Tang, M., Pearson, S.-A., Simes, R. J., & Chua, B. H. (2023) · Current Oncology · doi:10.3390/curroncol30020143 · Cited by 88
STK11 (LKB1) mutations in metastatic NSCLC: Prognostic value in the real world
Shire, N. J., Klein, A. B., Golozar, A., Collins, J. M., Fraeman, K. H., Nordstrom, B. L., McEwen, R., Hembrough, T., & Rizvi, N. A. (2020) · PLOS ONE · doi:10.1371/journal.pone.0238358 · Cited by 85
Azoulay, L. (2022) · The Oncologist · doi:10.1093/oncolo/oyac114 · Cited by 79
Halmos, B., Tan, E.-H., Soo, R. A., Cadranel, J., Lee, M. K., Foucher, P., Hsia, T.-C., Hochmair, M., Griesinger, F., Hida, T., et al. (2019) · Lung Cancer · doi:10.1016/j.lungcan.2018.10.028 · Cited by 74
Metadata
- Authors
- Khozin, S., Blumenthal, G. M., & Pazdur, R.
- Venue
- JNCI: Journal of the National Cancer Institute
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-09-13
- Volume
- 109
- Issue
- 11
- ISSN
- 0027-8874 (ISSN-L)
- 1460-2105
- OpenAlex
- W2756376311
- Cited by
- 346
- References
- 42
- FWCI
- 37.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Khozin, S., Blumenthal, G. M., & Pazdur, R. (2017). Real-world Data for Clinical Evidence Generation in Oncology. JNCI: Journal of the National Cancer Institute. https://doi.org/10.1093/jnci/djx187
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
