myrwdРеальная клиническая практика: данные и доказательстваReal-World Data & Evidence2782-3784Publishing House OKI10.37489/2782-3784-myrwd-49CKZYTJmyrwd-63Research ArticleПРАКТИЧЕСКИЕ РЕКОМЕНДАЦИИPRACTICAL RECOMMENDATIONSСТАТЬЯ ОТОЗВАНА: Практические рекомендации ESMO-GROW по составлению отчётов об RWE исследованиях в онкологииRETRACTED: ESMO Guidance for reporting oncology real-world evidence (GROW)https://orcid.org/0000-0003-2863-792XМотринчукА. Ш.MotrinchukA. Sh.

Мотринчук Айтэн Шерифовна — Ординатор кафедры клинической фармакологи и доказательной медицины

Санкт-Петербург

Aiten S. Motrinchuk — resident of the department of Clinical Pharmacology and Evidence-Based Medicine

St. Petersburg

aitesha555@yandex.ruhttps://orcid.org/0000-0003-3031-4572КурылевА. А.KurylevA. A.

Курылев Алексей Александрович — к. м. н., доцент кафедры клинической фармакологии и доказательной медицины

Санкт-Петербург

Alexey A. Kurylev — Cand. Sci. Med., associate professor Department of Clinical Pharmacology and Evidence-Based Medicine

St. Petersburg

alexey-kurilev@yandex.ruФГБОУ ВО «Первый Санкт-Петербургский государственный медицинский университет имени академика И. П. Павлова» Министерства здравоохранения Российской ФедерацииРоссияFirst St. Petersburg State Medical University named after academician I. P. PavlovRussian FederationФГБОУ ВО «Первый Санкт-Петербургский государственный медицинский университет имени академика И. П. Павлова» Министерства здравоохранения Российской Федерации; ФГБУ «Национальный исследовательский центр онкологии им. Н. Н. Петрова» Министерства здравоохранения Российской ФедерацииРоссияFirst St. Petersburg State Medical University named after academician I. P. Pavlov; National Medical Research Center of Oncology named after N. N. PetrovRussian Federation202430032024413244Copyright © Мотринчук А.Ш., Курылев А.А., 20242024Мотринчук А.Ш., Курылев А.А.Motrinchuk A.S., Kurylev A.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.myrwd.ru/jour/article/view/63

СТАТЬЯ ОТОЗВАНА

Европейское общество медицинской онкологии (ESMO) выпустило первое экспертное руководство по составлению отчётов о результатах исследований, направленных на получение доказательств, полученных из данных реальной клинической практики (RWE) специально для онкологии. В публикации рассматриваются нюансы современных RWE-исследований в онкологии, приводится полный список подробных ключевых рекомендаций, которые также были преобразованы в интерактивный информативный контрольный список для полноценной подготовки статей в различных сценариях RWE-исследований.

RETRACTED ARTICLE

The European Society for Medical Oncology (ESMO) has produced the very first expert-based guidance for reporting real-world evidence (RWE) studies specifically for oncology. The publication addresses nuances of modern RWE research in oncology and provides a comprehensive list of detailed key recommendations — that have also been transcribed into an interactive informative scoring checklist tool — for full article development in different RWE research scenarios.

ESMO-GROWрекомендации для специалистовонкологияданные реальной клинической практикиESMO-GROWrecommendationsoncologyRWERWDReferencesJin 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 Nov 20;35(33):3745-3752. doi: 10.1200/JCO.2017.73.4186.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 Nov 20;35(33):3745-3752. doi: 10.1200/JCO.2017.73.4186.Doody O, Bailey ME. Setting a research question, aim and objective. Nurse Res. 2016 Mar;23(4):19- 23. doi: 10.7748/nr.23.4.19.s5.Doody O, Bailey ME. Setting a research question, aim and objective. Nurse Res. 2016 Mar;23(4):19- 23. doi: 10.7748/nr.23.4.19.s5.Farrugia P, Petrisor BA, Farrokhyar F, Bhandari M. Practical tips for surgical research: Research questions, hypotheses and objectives. Can J Surg. 2010 Aug;53(4):278-81.Farrugia P, Petrisor BA, Farrokhyar F, Bhandari M. Practical tips for surgical research: Research questions, hypotheses and objectives. Can J Surg. 2010 Aug;53(4):278-81.Morgan RL, Whaley P, Thayer KA, Schünemann HJ. Identifying the PECO: A framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int. 2018 Dec;121(Pt 1):1027-1031. doi: 10.1016/j.envint.2018.07.015.Morgan RL, Whaley P, Thayer KA, Schünemann HJ. Identifying the PECO: A framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int. 2018 Dec;121(Pt 1):1027-1031. doi: 10.1016/j.envint.2018.07.015.Shmueli G. To explain or to predict? Stat Sci. 2010;25:289-310. https://doi.org/10.1214/10-STS330.Shmueli G. To explain or to predict? Stat Sci. 2010;25:289-310. https://doi.org/10.1214/10-STS330.The PROGRESS framework. https://www.prognosisresearch.com/progress-frameworkThe PROGRESS framework. https://www.prognosisresearch.com/progress-frameworkFelip E, Altorki N, Zhou C, Vallières E, Martínez- Martí A, Rittmeyer A, Chella A, Reck M, Goloborodko O, Huang M, Belleli R, McNally V, Srivastava MK, Bennett E, Gitlitz BJ, Wakelee HA. Overall survival with adjuvant atezolizumab after chemotherapy in resected stage II-IIIA non-smallcell lung cancer (IMpower010): a randomised, multicentre, open-label, phase III trial. Ann Oncol. 2023 Oct;34(10):907-919. doi: 10.1016/j.annonc. 2023.07.001.Felip E, Altorki N, Zhou C, Vallières E, Martínez- Martí A, Rittmeyer A, Chella A, Reck M, Goloborodko O, Huang M, Belleli R, McNally V, Srivastava MK, Bennett E, Gitlitz BJ, Wakelee HA. Overall survival with adjuvant atezolizumab after chemotherapy in resected stage II-IIIA non-smallcell lung cancer (IMpower010): a randomised, multicentre, open-label, phase III trial. Ann Oncol. 2023 Oct;34(10):907-919. doi: 10.1016/j.annonc. 2023.07.001.National Institute for Health and Care Excellence Methods for the development of NICE public health guidance (third edition). https://www.nice.org.uk/process/pmg4/chapter/appendix-e-algorithm-for-classifying-quantitative-experimental-and-observational-study-designsNational Institute for Health and Care Excellence Methods for the development of NICE public health guidance (third edition). https://www.nice.org.uk/process/pmg4/chapter/appendix-e-algorithm-for-classifying-quantitative-experimental-and-observational-study-designsRanganathan P, Aggarwal R. Study designs: Part 1 — An overview and classification. Perspect Clin Res. 2018 Oct-Dec;9(4):184-186. doi: 10.4103/picr.PICR_124_18.Ranganathan P, Aggarwal R. Study designs: Part 1 — An overview and classification. Perspect Clin Res. 2018 Oct-Dec;9(4):184-186. doi: 10.4103/picr.PICR_124_18.Chidambaram AG, Josephson M. Clinical research study designs: The essentials. Pediatr Investig. 2019 Dec 21;3(4):245-252. doi: 10.1002/ped4.12166.Chidambaram AG, Josephson M. Clinical research study designs: The essentials. Pediatr Investig. 2019 Dec 21;3(4):245-252. doi: 10.1002/ped4.12166.Oude Rengerink K, Kalkman S, Collier S, Ciaglia A, Worsley SD, Lightbourne A, Eckert L, Groenwold RHH, Grobbee DE, Irving EA; Work Package 3 of the GetReal consortium. Series: Pragmatic trials and real world evidence: Paper 3. Patient selection challenges and consequences. J Clin Epidemiol. 2017 Sep;89:173-180. doi: 10.1016/j.jclinepi.2016.12.021.Oude Rengerink K, Kalkman S, Collier S, Ciaglia A, Worsley SD, Lightbourne A, Eckert L, Groenwold RHH, Grobbee DE, Irving EA; Work Package 3 of the GetReal consortium. Series: Pragmatic trials and real world evidence: Paper 3. Patient selection challenges and consequences. J Clin Epidemiol. 2017 Sep;89:173-180. doi: 10.1016/j.jclinepi.2016.12.021.Penberthy LT, Rivera DR, Lund JL, Bruno MA, Meyer AM. An overview of real-world data sources for oncology and considerations for research. CA Cancer J Clin. 2022 May;72(3):287-300. doi: 10.3322/caac.21714.Penberthy LT, Rivera DR, Lund JL, Bruno MA, Meyer AM. An overview of real-world data sources for oncology and considerations for research. CA Cancer J Clin. 2022 May;72(3):287-300. doi: 10.3322/caac.21714.Booth CM, Karim S, Mackillop WJ. Real-world data: towards achieving the achievable in cancer care. Nat Rev Clin Oncol. 2019 May;16(5):312-325. doi: 10.1038/s41571-019-0167-7.Booth CM, Karim S, Mackillop WJ. Real-world data: towards achieving the achievable in cancer care. Nat Rev Clin Oncol. 2019 May;16(5):312-325. doi: 10.1038/s41571-019-0167-7.Arndt V, Stürmer T, Stegmaier C, Ziegler H, Dhom G, Brenner H. Patient delay and stage of diagnosis among breast cancer patients in Germany - a population based study. Br J Cancer. 2002 Apr 8;86(7):1034-40. doi: 10.1038/sj.bjc.6600209.Arndt V, Stürmer T, Stegmaier C, Ziegler H, Dhom G, Brenner H. Patient delay and stage of diagnosis among breast cancer patients in Germany - a population based study. Br J Cancer. 2002 Apr 8;86(7):1034-40. doi: 10.1038/sj.bjc.6600209.McKenzie F, Zietsman A, Galukande M, Anele A, Adisa C, Parham G, Pinder L, Cubasch H, Joffe M, Kidaaga F, Lukande R, Offiah AU, Egejuru RO, Shibemba A, Schuz J, Anderson BO, Dos Santos Silva I, McCormack V. Drivers of advanced stage at breast cancer diagnosis in the multicountry African breast cancer - disparities in outcomes (ABC-DO) study. Int J Cancer. 2018 Apr 15;142(8):1568-1579. doi: 10.1002/ijc.31187.McKenzie F, Zietsman A, Galukande M, Anele A, Adisa C, Parham G, Pinder L, Cubasch H, Joffe M, Kidaaga F, Lukande R, Offiah AU, Egejuru RO, Shibemba A, Schuz J, Anderson BO, Dos Santos Silva I, McCormack V. Drivers of advanced stage at breast cancer diagnosis in the multicountry African breast cancer - disparities in outcomes (ABC-DO) study. Int J Cancer. 2018 Apr 15;142(8):1568-1579. doi: 10.1002/ijc.31187.Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, Sørensen HT, von Elm E, Langan SM; RECORD Working Committee. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015 Oct 6;12(10):e1001885. doi: 10.1371/journal.pmed.1001885.Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, Sørensen HT, von Elm E, Langan SM; RECORD Working Committee. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015 Oct 6;12(10):e1001885. doi: 10.1371/journal.pmed.1001885.Manuel DG, Rosella LC, Stukel TA. Importance of accurately identifying disease in studies using electronic health records. BMJ. 2010 Aug 19;341:c4226. doi: 10.1136/bmj.c4226.Manuel DG, Rosella LC, Stukel TA. Importance of accurately identifying disease in studies using electronic health records. BMJ. 2010 Aug 19;341:c4226. doi: 10.1136/bmj.c4226.Sterne JA, Hernán MA, Reeves BC, Savović J, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016 Oct 12;355:i4919. doi: 10.1136/bmj.i4919.Sterne JA, Hernán MA, Reeves BC, Savović J, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016 Oct 12;355:i4919. doi: 10.1136/bmj.i4919.O'Connor JP, Aboagye EO, Adams JE, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017 Mar;14(3):169-186. doi: 10.1038/nrclinonc.2016.162.O'Connor JP, Aboagye EO, Adams JE, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017 Mar;14(3):169-186. doi: 10.1038/nrclinonc.2016.162.see Supplementary Material Section 2, TableS9, available at https://doi.org/10.1016/j.annonc.2023.10.001see Supplementary Material Section 2, TableS9, available at https://doi.org/10.1016/j.annonc.2023.10.001Wang R, Lagakos SW, Ware JH, Hunter DJ, Drazen JM. Statistics in medicine-reporting of subgroup analyses in clinical trials. N Engl J Med. 2007 Nov 22;357(21):2189-94. doi: 10.1056/NEJMsr077003.Wang R, Lagakos SW, Ware JH, Hunter DJ, Drazen JM. Statistics in medicine-reporting of subgroup analyses in clinical trials. N Engl J Med. 2007 Nov 22;357(21):2189-94. doi: 10.1056/NEJMsr077003.Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf. 2006 May;15(5):291-303. doi: 10.1002/pds.1200.Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf. 2006 May;15(5):291-303. doi: 10.1002/pds.1200.Hiemstra B. Keus F. Wetterslev J.et al. DEBATE- statistical analysis plans for observational studies. BMC Med Res Methodol.2019;19:233. https://doi.org/10.1186/s12874-019-0879-5.Hiemstra B. Keus F. Wetterslev J.et al. DEBATE- statistical analysis plans for observational studies. BMC Med Res Methodol.2019;19:233. https://doi.org/10.1186/s12874-019-0879-5.Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov. 2021 Apr;11(4):900-915. doi: 10.1158/2159-8290.CD-21-0090.Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov. 2021 Apr;11(4):900-915. doi: 10.1158/2159-8290.CD-21-0090.Skrede OJ, De Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, Askautrud HA, Pradhan M, Nesheim JA, Albregtsen F, Farstad IN, Domingo E, Church DN, Nesbakken A, Shepherd NA, Tomlinson I, Kerr R, Novelli M, Kerr DJ, Danielsen HE. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet. 2020 Feb 1;395(10221):350-360. doi: 10.1016/S0140-6736(19)32998-8.Skrede OJ, De Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, Askautrud HA, Pradhan M, Nesheim JA, Albregtsen F, Farstad IN, Domingo E, Church DN, Nesbakken A, Shepherd NA, Tomlinson I, Kerr R, Novelli M, Kerr DJ, Danielsen HE. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet. 2020 Feb 1;395(10221):350-360. doi: 10.1016/S0140-6736(19)32998-8.Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018 Apr;15(4):233-234. doi: 10.1038/nmeth.4642.Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018 Apr;15(4):233-234. doi: 10.1038/nmeth.4642.European Commission the Assessment List for Trustworthy Artificial Intelligence. https://altai.insight-centre.org/European Commission the Assessment List for Trustworthy Artificial Intelligence. https://altai.insight-centre.org/US Food and Drug Administration. Good Machine Learning Practice for Medical Device Development: Guiding Principles. https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principlesUS Food and Drug Administration. Good Machine Learning Practice for Medical Device Development: Guiding Principles. https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principlesUS Food and Drug Administration. Artificial Intelligence and Machine Learning in Software as a Medical Device. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-deviceUS Food and Drug Administration. Artificial Intelligence and Machine Learning in Software as a Medical Device. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-deviceGroenwold RHH, Dekkers OM. Missing data: the impact of what is not there. Eur J Endocrinol. 2020 Oct;183(4):E7-E9. doi: 10.1530/EJE-20-0732.Groenwold RHH, Dekkers OM. Missing data: the impact of what is not there. Eur J Endocrinol. 2020 Oct;183(4):E7-E9. doi: 10.1530/EJE-20-0732.Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JP. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016 Oct 12;355:i4919. doi: 10.1136/bmj.i4919.Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JP. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016 Oct 12;355:i4919. doi: 10.1136/bmj.i4919.ROBINS-E Development Group Risk Of Bias In Non-randomized Studies — of Exposure (ROBINS-E). Launch version. https://www.riskofbias.info/welcome/robins-e-toolROBINS-E Development Group Risk Of Bias In Non-randomized Studies — of Exposure (ROBINS-E). Launch version. https://www.riskofbias.info/welcome/robins-e-toolRanganathan P, Pramesh CS, Buyse M. Common pitfalls in statistical analysis: Clinical versus statistical significance. Perspect Clin Res. 2015 Jul-Sep;6(3):169-70. doi: 10.4103/2229-3485.159943.Ranganathan P, Pramesh CS, Buyse M. Common pitfalls in statistical analysis: Clinical versus statistical significance. Perspect Clin Res. 2015 Jul-Sep;6(3):169-70. doi: 10.4103/2229-3485.159943.

The authors declare that there are no conflicts of interest present.