ESMO Guidance for Reporting Oncology real-World evidence (GROW)

ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

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Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

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The Reporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) statement.

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To Explain or to Predict?

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Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.

Hernán, M. A., & Robins, J. M. (2016) · American Journal of Epidemiology · doi:10.1093/aje/kwv254 · Cited by 3,115

Statistics versus machine learning

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Statistics in Medicine — Reporting of Subgroup Analyses in Clinical Trials

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Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers

Mongan, J., Moy, L., & Kahn, C. E. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2020200029 · Cited by 1,443

REporting recommendations for tumour MARKer prognostic studies (REMARK).

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Identifying the PECO: A framework for formulating good questions to explore the association of environmental and other exposures with health outcomes

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Imaging biomarker roadmap for cancer studies

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Eligibility Criteria of Randomized Controlled Trials Published in High-Impact General Medical Journals

Spall, H. G. C. V., Toren, A., Kiss, A., & Fowler, R. (2007) · JAMA · doi:10.1001/jama.297.11.1233 · Cited by 1,054

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01772-9 · Cited by 1,039

SMART on FHIR: a standards-based, interoperable apps platform for electronic health records

Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., & Ramoni, R. B. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv189 · Cited by 829

Artificial Intelligence in Cancer Research and Precision Medicine

Bhinder, B., Gilvary, C., Madhukar, N. S., & Elemento, O. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0090 · Cited by 781

Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics

Schneeweiß, S. (2006) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1200 · Cited by 741

Guidelines for Reporting Outcomes in Trial Reports: The CONSORT-Outcomes 2022 Extension

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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension

Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., & Denniston, A. K. (2020) · BMJ · doi:10.1136/bmj.m3164 · Cited by 702

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · BMJ · doi:10.1136/bmj-2022-070904 · Cited by 665

Descriptive studies: what they can and cannot do

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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

Rivera, S. C., Liu, X., Chan, A.-W., Denniston, A. K., & Calvert, M. J. (2020) · BMJ · doi:10.1136/bmj.m3210 · Cited by 463

Emulation of Randomized Clinical Trials With Nonrandomized Database Analyses: Results of 32 Clinical Trials

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Real-world Data for Clinical Evidence Generation in Oncology

Khozin, S., Blumenthal, G. M., & Pazdur, R. (2017) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djx187 · Cited by 361

Real-world data: towards achieving the achievable in cancer care

Booth, C. M., Karim, S., & Mackillop, W. J. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0167-7 · Cited by 349

Designing deep learning studies in cancer diagnostics

Kleppe, A., Skrede, O.-J., Raedt, S. D., Liestøl, K., Kerr, D. J., & Danielsen, H. E. (2021) · Nature reviews. Cancer · doi:10.1038/s41568-020-00327-9 · Cited by 328

STrengthening the REporting of Genetic Association studies (STREGA) – an extension of the STROBE statement

Little, J., Higgins, J. P. T., Ioannidis, J. P. A., Moher, D., Gagnon, F., von Elm, E. B., Khoury, M. J., Cohen, B. J., Smith, G. D., Grimshaw, J., et al. (2009) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2009.02125.x · Cited by 327

FAIR data enabling new horizons for materials research

Scheffler, M., Aeschlimann, M., Albrecht, M., Bereau, T., Bungartz, H., Felser, C., Greiner, M. T., Groß, A., Koch, C. T., Kremer, K., et al. (2022) · Nature · doi:10.1038/s41586-022-04501-x · Cited by 271

Common pitfalls in statistical analysis: Clinical versus statistical significance

Ranganathan, P., Pramesh, C. S., & Buyse, M. E. (2015) · Perspectives in Clinical Research · doi:10.4103/2229-3485.159943 · Cited by 268

Patient delay and stage of diagnosis among breast cancer patients in Germany – a population based study

Arndt, V., Stürmer‎, T., Stegmaier, C., Ziegler, H., Dhom, G., & Brenner, H. (2002) · British Journal of Cancer · doi:10.1038/sj.bjc.6600209 · Cited by 267

Re-Evaluating Eligibility Criteria for Oncology Clinical Trials: Analysis of Investigational New Drug Applications in 2015

Jin, S., Pazdur, R., & Sridhara, R. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2017.73.4186 · Cited by 220

STaRT-RWE: structured template for planning and reporting on the implementation of real world evidence studies

Wang, S. V., Pinheiro, S. P., Hua, W., Arlett, P. R., Uyama, Y., Berlin, J. A., Bartels, D. B., Kahler, K. H., Bessette, L. G., & Schneeweiß, S. (2021) · BMJ · doi:10.1136/bmj.m4856 · Cited by 217

Study designs: Part 1 – An overview and classification

Ranganathan, P., & Aggarwal, R. (2018) · Perspectives in Clinical Research · doi:10.4103/picr.picr_124_18 · Cited by 189

Real-World Evidence in Oncology: Opportunities and Limitations

Maïo, M. D., Perrone, F., & Conte, P. (2019) · The Oncologist · doi:10.1634/theoncologist.2019-0647 · Cited by 181

Importance of accurately identifying disease in studies using electronic health records

Manuel, D. G., Rosella, L. C., & Stukel, T. A. (2010) · BMJ · doi:10.1136/bmj.c4226 · Cited by 159

Practical tips for surgical research: Research questions, hypotheses and objectives.

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Drivers of advanced stage at breast cancer diagnosis in the multicountry A frican breast cancer – disparities in outcomes (ABC‐DO) study

McKenzie, F., Zietsman, A., Galukande, M., Anele, A. A., Adisa, C., Parham, G. P., Pinder, L. F., Cubasch, H., Joffe, M., Kidaaga, F., et al. (2017) · International Journal of Cancer · doi:10.1002/ijc.31187 · Cited by 107

Setting a research question, aim and objective

Doody, O., & Bailey, M. (2016) · Nurse Researcher · doi:10.7748/nr.23.4.19.s5 · Cited by 103

Defining the role of real-world data in cancer clinical research: The position of the European Organisation for Research and Treatment of Cancer

Saesen, R., Hemelrijck, M. V., Bogaerts, J., Booth, C. M., Cornelissen, J. J., Dekker, A. L. A. J., Eisenhauer, E. A., Freitas, A., Gronchi, A., Hernán, M. Á., et al. (2023) · European Journal of Cancer · doi:10.1016/j.ejca.2023.03.013 · Cited by 102

An overview of real‐world data sources for oncology and considerations for research

Penberthy, L., Rivera, D. R. R., Lund, J. L., Bruno, M. A., & Meyer, A. (2021) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21714 · Cited by 99

Series: Pragmatic trials and real world evidence: Paper 3. Patient selection challenges and consequences

Rengerink, K. O., Kalkman, S., Collier, S., Ciaglia, A., Worsley, S. D., Lightbourne, A., Eckert, L., Groenwold, R. H. H., Grobbee, D. E., & Irving, E. A. (2017) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.12.021 · Cited by 88

Rationale, Strengths, and Limitations of Real-World Evidence in Oncology: A Canadian Review and Perspective

Azoulay, L. (2022) · The Oncologist · doi:10.1093/oncolo/oyac114 · Cited by 79

A newly developed tool for classifying study designs in systematic reviews of interventions and exposures showed substantial reliability and validity

Seo, H.-J., Kim, S. Y., Lee, Y. J., Jang, B.-H., Park, J.-E., Sheen, S.-S., & Hahn, S. K. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.09.013 · Cited by 74

Prospective or retrospective: what's in a name?

Vandenbroucke, J. P. (1991) · BMJ · doi:10.1136/bmj.302.6771.249 · Cited by 71

Real-World Evidence in Support of Oncology Product Registration: A Systematic Review of New Drug Application and Biologics License Application Approvals from 2015–2020

Arondekar, B., Duh, M. S., Bhak, R. H., DerSarkissian, M., Huynh, L., Wang, K., Wojciehowski, J., Wu, M., Wornson, B., Niyazov, A., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-2639 · Cited by 71

How are missing data in covariates handled in observational time-to-event studies in oncology? A systematic review

Carroll, O., Morris, T. P., & Keogh, R. H. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-01018-7 · Cited by 62

Clinical research study designs: The essentials

Chidambaram, A. G., & Josephson, M. B. (2019) · Pediatric Investigation · doi:10.1002/ped4.12166 · Cited by 60

Implications of missing data on reported breast cancer mortality

Plichta, J. K., Rushing, C. N., Lewis, H. C., Rooney, M. M., Blazer, D. G., Thomas, S. M., Hwang, E. S., & Greenup, R. A. (2022) · Breast Cancer Research and Treatment · doi:10.1007/s10549-022-06764-4 · Cited by 60

Missing data: the impact of what is not there

Groenwold, R. H. H., & Dekkers, O. M. (2020) · European Journal of Endocrinology · doi:10.1530/eje-20-0732 · Cited by 59

Organized structure of real-world evidence best practices: moving from fragmented recommendations to comprehensive guidance

Jaksa, A. A., Wu, J. J., Jonsson, P. F., Eichler, H., Vititoe, S. E., & Gatto, N. M. (2021) · Journal of Comparative Effectiveness Research · doi:10.2217/cer-2020-0228 · Cited by 58

HARmonized Protocol Template to Enhance Reproducibility of Hypothesis Evaluating Real-World Evidence Studies on Treatment Effects: A Good Practices Report of a Joint ISPE/ISPOR Task Force

Wang, S. V., Pottegård, A., Crown, W. H., Arlett, P. R., Ashcroft, D. M., Benchimol, E. I., Berger, M. L., Crane, G., Goettsch, W., Hua, W., et al. (2022) · Value in Health · doi:10.1016/j.jval.2022.09.001 · Cited by 54

DEBATE-statistical analysis plans for observational studies

Hiemstra, B., Keus, F., Wetterslev, J., Gluud, C. N., & van der Horst, I. C. C. (2019) · BMC Medical Research Methodology · doi:10.1186/s12874-019-0879-5 · Cited by 52

Application of Real-World Data to External Control Groups in Oncology Clinical Trial Drug Development

Yap, T. A., Jacobs, I. A., Andre, E. B., Lee, L. J., Beaupre, D. M., & Azoulay, L. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2021.695936 · Cited by 47

Real-world outcomes associated with new cancer medicines approved by the Food and Drug Administration and European Medicines Agency: A retrospective cohort study

Boyle, J. M., Hegarty, G., FRAMPTON, C. M. A., Harvey-Jones, E. J., Dodkins, J., Beyer, K., George, G., Sullivan, R. J., Booth, C. M., & Aggarwal, A. K. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.07.001 · Cited by 46

STrengthening the Reporting of OBservational studies in Epidemiology: Molecular Epidemiology STROBE-ME. An extension of the STROBE statement

Gallo, V., Egger, M., McCormack, V., Farmer, P. B., Ioannidis, J. P. A., Kirsch‐Volders, M., Matullo, G., Phillips, D. H., Schoket, B., Strömberg, U., et al. (2011) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2011-200318 · Cited by 29

Clinical Application of Computational Methods in Precision Oncology

Panagiotou, O. A., Hoffman-Hōgg, L., Hricak, H., Khleif, S. N., Levy, M. A., Magnus, D. C., Murphy, M. J., Patel, B., Winn, R. A., Nass, S. J., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.1247 · Cited by 28

Exploratory analyses in aetiologic research and considerations for assessment of credibility: mini-review of literature

Luijken, K., Dekkers, O. M., Rosendaal, F. R., & Groenwold, R. H. H. (2022) · BMJ · doi:10.1136/bmj-2021-070113 · Cited by 28

Randomized clinical trial eligibility rates for chemotherapy (CT) and antiangiogenic therapy (AAT) in a population-based cohort of newly diagnosed non-small cell lung cancer (NSCLC) patients

Fehrenbacher, L., Ackerson, L., & Somkin, C. P. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2009.27.15_suppl.6538 · Cited by 21

The role of Real-World Data and evidence in oncology medicines approved in EU in 2018–2019

Eskola, S. M., Leufkens, H. G. M., Bate, A., Bruin, M. L. D., & Gardarsdóttir, H. (2023) · Journal of Cancer Policy · doi:10.1016/j.jcpo.2023.100424 · Cited by 20

Measuring cause-and-effect relationships without randomized clinical trials: Quasi-experimental methods for gynecologic oncology research

Moss, H. A., Melamed, A., & Wright, J. D. (2019) · Gynecologic Oncology · doi:10.1016/j.ygyno.2018.11.006 · Cited by 18

Young oncologists’ perspective on the role and future of the clinician-scientist in oncology

Lim, K. H. J., Westphalen, C. B., Berghoff, A. S., Cardone, C., Connolly, E. A., Güven, D. C., Kfoury, M., Kocakavuk, E., Mandó, P., Mariamidze, E., et al. (2023) · ESMO Open · doi:10.1016/j.esmoop.2023.101625 · Cited by 15

1689O Comprehensive mapping review of real-world evidence publications focusing on targeted therapies in solid tumors: A collaborative work from ESMO real-world data and Digital Health Working Group

Pellat, A., Grinda, T., Prelaj, A., Cresta, P., Valachis, A., Zerdes, I., Martins-Branco, D. A., Provenzano, L., Spagnoletti, A., Marta, G. N., et al. (2023) · Annals of Oncology · doi:10.1016/j.annonc.2023.09.2643 · Cited by 14

Do overly complex reporting guidelines remove the focus from good clinical trials?

Howick, J., Webster, R., Knottnerus, J. A., & Moher, D. (2021) · BMJ · doi:10.1136/bmj.n1793 · Cited by 13

Evaluation of reporting quality of cohort studies using real-world data based on RECORD: systematic review

Zhao, R., Zhang, W., Zhang, Z., He, C., Xu, R., Tang, X., & Wang, B. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-01960-2 · Cited by 8

BCLC strategy for prognosis prediction and treatment recommendations: The 2026 update

Reig, M., Sanduzzi‐Zamparelli, M., Forner, A., Rimola, J., Ferrer, J., Burrel, M., García‐Criado, Á., Díaz, A., Llarch, N., Iserte, G., et al. (2025) · Journal of Hepatology · doi:10.1016/j.jhep.2025.10.020 · Cited by 253

A guide to artificial intelligence for cancer researchers

Pérez-López, R., Laleh, N. G., Mahmood, F., & Kather, J. N. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00694-7 · Cited by 227

Advances in molecular pathology and therapy of non-small cell lung cancer

Huang, Q., Li, Y., Huang, Y., Wu, J., Bao, W., Xue, C., Li, X., Dong, S., Dong, Z., & Hu, S. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02243-6 · Cited by 197

Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions

Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196

Privacy-preserving large language models for structured medical information retrieval

Wiest, I. C., Ferber, D., Zhu, J., van Treeck, M., Meyer, S. K., Juglan, R., Carrero, Z. I., Paech, D., Kleesiek, J., Ebert, M., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01233-2 · Cited by 184

Real-World Evidence in Oncology: Opportunities and Limitations

Maïo, M. D., Perrone, F., & Conte, P. (2019) · The Oncologist · doi:10.1634/theoncologist.2019-0647 · Cited by 181

Pré-test

Pellat, A., & Grinda, T. (2024) · Hépato-Gastro & Oncologie Digestive · doi:10.1684/hpg.2024.2812

Real-world evidence studies in oncology: a trend or a necessity?

Zhuravkov, A., Moiseenko, F. V., Колбин, А. С., & Moiseyenko, V. M. (2023) · Practical oncology · doi:10.31917/2401059 · Cited by 3

Longitudinal trends of real-world evidence (RWE) reporting in oncology conferences: An 11-year ASCO Annual Meeting analysis.

Azad, F., Zhang, J., Akhtar, O. S., George, A. M., Attwood, K. M., & Torka, P. (2022) · Journal of Clinical Oncology · doi:10.1200/jco.2022.40.16_suppl.e18748 · Cited by 1

Editorial: Real-world data and real-world evidence in lung cancer

Gristina, V., & Eze, C. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1436077 · Cited by 9