Interpretation of ESMO guidelines for reporting real-world evidence in oncology (ESMO-GROW checklist)_Chinese Journal of Evidence-Based Medicine

Authors：

CHEN Rong ,  LUO Ling , ZHANG Yunzhi , LIU Zhen , LIU Anlin , ZHANG Yiwen

Department of Infectious Diseases, the Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, P. R. China;

Corresponding author：

LUO Ling, Email: LL7765@cqmu.edu.cn

Keywords：

Real-world data; Real-world evidence; Oncology research; Reporting guideline; Interpretation

DOI：

10.7507/1672-2531.202402101

Video：

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To enhance the quality and transparency of oncology real-world evidence studies, the European Society for Medical Oncology (ESMO) has developed the first specific reporting guidelines for oncology RWE studies in peer-reviewed journals "the ESMO Guidance for Reporting Oncology Real-World Evidence (GROW)". To facilitate readers understanding and application of these reporting standards, this article introduces and interprets the development process and main contents of the ESMO-GROW checklist.

Citation： CHEN Rong, LUO Ling, ZHANG Yunzhi, LIU Zhen, LIU Anlin, ZHANG Yiwen. Interpretation of ESMO guidelines for reporting real-world evidence in oncology (ESMO-GROW checklist). Chinese Journal of Evidence-Based Medicine, 2025, 25(1): 102-110. doi: 10.7507/1672-2531.202402101 Copy

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2.	Malagola M, Ohgami R, Greco R. Editorial: real-world data and real-world evidence in hematologic malignancies. Front Oncol, 2023, 13: 1232980.
3.	Arondekar B, Duh MS, Bhak RH, et al. Real-world evidence in support of oncology product registration: a systematic review of new drug application and biologics license application approvals from 2015-2020. Clin Cancer Res, 2022, 28(1): 27-35.
4.	Eskola SM, Leufkens HGM, Bate A, et al. The role of real-world data and evidence in oncology medicines approved in EU in 2018-2019. J Cancer Policy, 2023, 36: 100424.
5.	Booth CM, Karim S, Mackillop WJ. Real-world data: towards achieving the achievable in cancer care. Nat Rev Clin Oncol, 2019, 16(5): 312-325.
6.	Rudrapatna VA, Butte AJ. Opportunities and challenges in using real-world data for health care. J Clin Invest, 2020, 130(2): 565-574.
7.	Castelo-Branco L, Pellat A, Martins-Branco D, et al. ESMO guidance for reporting oncology real-world evidence (GROW). Ann Oncol, 2023, 34(12): 1097-1112.
8.	Yap TA, Jacobs I, Baumfeld Andre E, et al. Application of real-world data to external control groups in oncology clinical trial drug development. Front Oncol, 2022, 11: 695936.
9.	Jin S, Pazdur R, Sridhara R. Re-evaluating eligibility criteria for oncology clinical trials: analysis of investigational new drug applications in 2015. J Clin Oncol, 2017, 35(33): 3745-3752.
10.	Doody O, Bailey ME. Setting a research question, aim and objective. Nurse Res, 2016, 23(4): 19-23.
11.	Morgan RL, Whaley P, Thayer KA, et al. Identifying the PECO: a framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int, 2018, 121(Pt 1): 1027-1031.
12.	Shmueli G. To explain or to predict. Stat Sci, 2010, 25(3): 289-310.
13.	Seo HJ, Kim SY, Lee YJ, et al. A newly developed tool for classifying study designs in systematic reviews of interventions and exposures showed substantial reliability and validity. J Clin Epidemiol, 2016, 70: 200-205.
14.	Oude Rengerink K, Kalkman S, Collier S, et al. Series: pragmatic trials and real world evidence: paper 3. Patient selection challenges and consequences. J Clin Epidemiol, 2017, 89: 173-180.
15.	Van Spall HG, Toren A, Kiss A, et al. Eligibility criteria of randomized controlled trials published in high-impact general medical journals: a systematic sampling review. JAMA, 2007, 297(11): 1233-1240.
16.	Di Maio M, Perrone F, Conte P. Real-world evidence in oncology: opportunities and limitations. Oncologist, 2020, 25(5): e746-e752.
17.	McKenzie F, Zietsman A, Galukande M, et al. Drivers of advanced stage at breast cancer diagnosis in the multicountry African breast cancer - disparities in outcomes (ABC-DO) study. Int J Cancer, 2018, 142(8): 1568-1579.
18.	Benchimol EI, Smeeth L, Guttmann A, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med, 2015, 12(10): e1001885.
19.	Manuel DG, Rosella LC, Stukel TA. Importance of accurately identifying disease in studies using electronic health records. BMJ, 2010, 341: c4226.
20.	Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf, 2006, 15(5): 291-303.
21.	Hiemstra B, Keus F, Wetterslev J, et al. DEBATE-statistical analysis plans for observational studies. BMC Med Res Methodol, 2019, 19(1): 233.
22.	卢娜, 孙劲楠, 丁佐奇, 等. 癌症领域的人工智能应用综述. 药物生物技术, 2023, 30(5): 538-550.
23.	Bhinder B, Gilvary C, Madhukar NS, et al. Artificial intelligence in cancer research and precision medicine. Cancer Discov, 2021, 11(4): 900-915.
24.	Kleppe A, Skrede OJ, De Raedt S, et al. Designing deep learning studies in cancer diagnostics. Nat Rev Cancer, 2021, 21(3): 199-211.
25.	Jia X, Ren L, Cai J. Clinical implementation of AI technologies will require interpretable AI models. Med Phys, 2020, 47(1): 1-4.
26.	Plichta JK, Rushing CN, Lewis HC, et al. Implications of missing data on reported breast cancer mortality. Breast Cancer Res Treat, 2023, 197(1): 177-187.
27.	Groenwold R, Dekkers OM. Missing data: the impact of what is not there. Eur J Endocrinol, 2020, 183(4): E7-E9.
28.	Sánchez JC, Picola N, Rodriguez-Vida A, et al. Apalutamide for prostate cancer: multicentre and multidisciplinary real-world study of 227 patients. Cancer Med, 2023, 12(24): 21969-21977.

1. Dang A. Real-world evidence: a primer. Pharmaceut Med, 2023, 37(1): 25-36.
2. Malagola M, Ohgami R, Greco R. Editorial: real-world data and real-world evidence in hematologic malignancies. Front Oncol, 2023, 13: 1232980.
3. Arondekar B, Duh MS, Bhak RH, et al. Real-world evidence in support of oncology product registration: a systematic review of new drug application and biologics license application approvals from 2015-2020. Clin Cancer Res, 2022, 28(1): 27-35.
4. Eskola SM, Leufkens HGM, Bate A, et al. The role of real-world data and evidence in oncology medicines approved in EU in 2018-2019. J Cancer Policy, 2023, 36: 100424.
5. Booth CM, Karim S, Mackillop WJ. Real-world data: towards achieving the achievable in cancer care. Nat Rev Clin Oncol, 2019, 16(5): 312-325.
6. Rudrapatna VA, Butte AJ. Opportunities and challenges in using real-world data for health care. J Clin Invest, 2020, 130(2): 565-574.
7. Castelo-Branco L, Pellat A, Martins-Branco D, et al. ESMO guidance for reporting oncology real-world evidence (GROW). Ann Oncol, 2023, 34(12): 1097-1112.
8. Yap TA, Jacobs I, Baumfeld Andre E, et al. Application of real-world data to external control groups in oncology clinical trial drug development. Front Oncol, 2022, 11: 695936.
9. Jin S, Pazdur R, Sridhara R. Re-evaluating eligibility criteria for oncology clinical trials: analysis of investigational new drug applications in 2015. J Clin Oncol, 2017, 35(33): 3745-3752.
10. Doody O, Bailey ME. Setting a research question, aim and objective. Nurse Res, 2016, 23(4): 19-23.
11. Morgan RL, Whaley P, Thayer KA, et al. Identifying the PECO: a framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int, 2018, 121(Pt 1): 1027-1031.
12. Shmueli G. To explain or to predict. Stat Sci, 2010, 25(3): 289-310.
13. Seo HJ, Kim SY, Lee YJ, et al. A newly developed tool for classifying study designs in systematic reviews of interventions and exposures showed substantial reliability and validity. J Clin Epidemiol, 2016, 70: 200-205.
14. Oude Rengerink K, Kalkman S, Collier S, et al. Series: pragmatic trials and real world evidence: paper 3. Patient selection challenges and consequences. J Clin Epidemiol, 2017, 89: 173-180.
15. Van Spall HG, Toren A, Kiss A, et al. Eligibility criteria of randomized controlled trials published in high-impact general medical journals: a systematic sampling review. JAMA, 2007, 297(11): 1233-1240.
16. Di Maio M, Perrone F, Conte P. Real-world evidence in oncology: opportunities and limitations. Oncologist, 2020, 25(5): e746-e752.
17. McKenzie F, Zietsman A, Galukande M, et al. Drivers of advanced stage at breast cancer diagnosis in the multicountry African breast cancer - disparities in outcomes (ABC-DO) study. Int J Cancer, 2018, 142(8): 1568-1579.
18. Benchimol EI, Smeeth L, Guttmann A, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med, 2015, 12(10): e1001885.
19. Manuel DG, Rosella LC, Stukel TA. Importance of accurately identifying disease in studies using electronic health records. BMJ, 2010, 341: c4226.
20. Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf, 2006, 15(5): 291-303.
21. Hiemstra B, Keus F, Wetterslev J, et al. DEBATE-statistical analysis plans for observational studies. BMC Med Res Methodol, 2019, 19(1): 233.
22. 卢娜, 孙劲楠, 丁佐奇, 等. 癌症领域的人工智能应用综述. 药物生物技术, 2023, 30(5): 538-550.
23. Bhinder B, Gilvary C, Madhukar NS, et al. Artificial intelligence in cancer research and precision medicine. Cancer Discov, 2021, 11(4): 900-915.
24. Kleppe A, Skrede OJ, De Raedt S, et al. Designing deep learning studies in cancer diagnostics. Nat Rev Cancer, 2021, 21(3): 199-211.
25. Jia X, Ren L, Cai J. Clinical implementation of AI technologies will require interpretable AI models. Med Phys, 2020, 47(1): 1-4.
26. Plichta JK, Rushing CN, Lewis HC, et al. Implications of missing data on reported breast cancer mortality. Breast Cancer Res Treat, 2023, 197(1): 177-187.
27. Groenwold R, Dekkers OM. Missing data: the impact of what is not there. Eur J Endocrinol, 2020, 183(4): E7-E9.
28. Sánchez JC, Picola N, Rodriguez-Vida A, et al. Apalutamide for prostate cancer: multicentre and multidisciplinary real-world study of 227 patients. Cancer Med, 2023, 12(24): 21969-21977.

Chinese Journal of Evidence-Based Medicine

Interpretation of ESMO guidelines for reporting real-world evidence in oncology (ESMO-GROW checklist)

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