References

myrwd

Реальная клиническая практика: данные и доказательства

Real-World Data & Evidence

2782-3784

Publishing House OKI

10.37489/2782-3784-myrwd-26

XKBEEQ

myrwd-30

Research Article

ОЦЕНКА ТЕХНОЛОГИЙ ЗДРАВООХРАНЕНИЯ

HEALTH TECHNOLOGY ASSESSMENT

Роль RWD / RWE в оценке технологий здравоохранения

Role of RWD / RWE in health technology assessment

https://orcid.org/0000-0002-4180-0878

Боровская

В. Г.

Borovskaya

V. G.

Боровская Валентина Геннадьевна — врач- терапевт, ординатор кафедры клинической фармакологии и доказательной медицины

Санкт-Петербург

St. Petersburg

valentine.borovskaya@gmail.com

https://orcid.org/0000-0003-3031-4572

Курылев

А. А.

Kurylev

A. A.

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

Санкт-Петербург

St. Petersburg

alexey-kurilev@yandex.ru

ФГБОУ ВО «Первый Санкт-Петербургский государственный медицинский университет имени И. П. Павлова»РоссияFSBEI HE First St. Petersburg State Medical University named after I. P. PavlovRussian Federation

ФГБОУ ВО «Первый Санкт-Петербургский государственный медицинский университет имени И. П. Павлова»; ФГБУ «Национальный исследовательский центр онкологии им. Н. Н. Петрова» Минздрава РоссииРоссияFSBEI HE First St. Petersburg State Medical University named after I. P. Pavlov; FSBI National Research Center of Oncology named after N. N. Petrov of the Ministry of Health of RussiaRussian Federation

2023

12032023

3118

2023

Боровская В.Г., Курылев А.А.

Borovskaya V.G., 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/30

Ввиду ряда причин рандомизированные клинические исследования на сегодняшний день не могут справиться с теми задачами, которые ставит современное здравоохранение, что было ярко продемонстрировано пандемией COVID-19. Это дало новый импульс для развития направления RWD / RWE, тогда стало ясно, что в случае невозможности, неэтичности и других причин можно эффективно пользоваться новыми методиками получения качественных доказательств, которые в дальнейшем могут использоваться лицами, принимающими решения, и организациями, занимающимися оценкой технологий здравоохранения.

For many reasons randomized clinical trials today can»t cope with the tasks set by modern health care, which was clearly demonstrated by the COVID-19 pandemic. This gave new impetus to the development of the RWD / RWE. It became obviously that in cases of impossibility, unethical and other reasons, new methods for obtaining high-quality evidence may be effectively used, and later can be used by decision-makers and organizations engaged in the assessment of health technologies.

данные реальной клинической практикиRWDдоказательства реальной клинической практикиRWEоценка технологий здравоохраненияОТЗCOVID-19

real-world dataRWDreal-world evidenceRWEhealth technology assessmentHTACOVID-19

References1

Журавков А. А., Колбин А. С. Внешний контроль при проведении исследований RWD / RWE: методологический подход. Качественная клиническая практика. 2022;(2):21-7. https://doi.org/10.37489/2588-0519-2022-2-21-27.

Zhuravkov AA, Kolbin AS. External control during RWD / RWE research: a methodological approach. Kachestvennaya klinicheskaya praktika = Good Clinical Practice. 2022; (2):21-7. (In Russ.)].

Новодережкина Е. А., Зырянов С. К. Значение исследований реальной клинической практики в оценке технологий здравоохранения. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2022;15(3):380-9. https://doi.org/10.17749/2070-4909/farmakoekonomika. 2022.120.

Novoderezhkina EA, Zyryanov SК. The role of real-world data and real-world evidence in health technology assessment. FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2022;15 (3):380-9. (In Russ.)].

Simpson A, Ramagopalan SV. R WE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment: part 10. J Comp Eff Res. 2023 Jan; 12 (1):e220194. doi: 10.2217/cer-2022-0194. Epub 2022 Dec 1. PMID: 36453665.

Simpson A, Ramagopalan SV. R WE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment: part 4. J Comp Eff Res. 2022 Jan; 11 (1):11-2. doi: 10.2217/cer-2021-0243. Epub 2021 Oct 27. PMID: 34702048.

Jaksa A, Wu J, Jónsson P, Eichler HG, Vititoe S, Gatto NM. Organized structure of real-world evidence best practices: moving from fragmented recommendations to comprehensive guidance. J Comp Eff Res. 2021 Jun;10 (9):711-31. doi: 10.2217/cer-2020-0228. Epub 2021 Apr 30. PMID: 33928789.

Kent S, Salcher-Konrad M, Boccia S, Bouvy JC, Waure C, Espin J, Facey K, Nguyen M, RejonParrilla JC, Jonsson P. The use of nonrandomized evidence to estimate treatment effects in health technology assessment. J Comp Eff Res. 2021 Oct;10 (14):1035-43. doi: 10.2217/cer-2021-0108. Epub 2021 Jul 19. PMID: 34279114.

National Institute for Health and Care Excellence. NICE real-world evidence framework (2022). https://www.nice.org.uk/corporate/ecd9/chapter/overview.

Simpson A, Ramagopalan SV. R WE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment: part 9. J Comp Eff Res. 2022 Nov; 11 (16):1147-9. doi: 10.2217/cer-2022-0145. Epub 2022 Aug 23. PMID: 35998008.

Taquet M, Geddes JR, Husain M, Luciano S, Harrison PJ. 6-month neurological and psychiatric outcomes in 236-379 survivors of COVID-19: a retrospective cohort study using electronic health records. Lancet Psychiatry. 2021 May; 8(5):416-27. doi: 10.1016/S2215–0366 (21) 00084-5. Epub 2021 Apr 6. PMID: 33836148; PMCID: PMC8023694.

Prats-Uribe A, Sena AG, Lai LYH, Ahmed WU, Alghoul H, Alser O, Alshammari TM, Areia C, Carter W, Casajust P, Dawoud D, Golozar A, Jonnagaddala J, Mehta PP, Gong M, Morales DR, Nyberg F, Posada JD, Recalde M, Roel E, Shah K, Shah NH, Schilling LM, Subbian V, Vizcaya D, Zhang L, Zhang Y, Zhu H, Liu L, Cho J, Lynch KE, Matheny ME, You SC, Rijnbeek PR, Hripcsak G, Lane JC, Burn E, Reich C, Suchard MA, Duarte-Salles T, Kostka K, Ryan PB, Prieto-Alhambra D. Use of repurposed and adjuvant drugs in hospital patients with covid-19: multinational network cohort study. BMJ. 2021 May 11;373: n1038. doi: 10.1136/bmj.n1038. Erratum in: BMJ. 2021 May 21;373: n1277. PMID: 33975825; PMCID: PMC8111167.

Mathur R, Rentsch CT, Morton CE, Hulme WJ, Schultze A, MacKenna B, Eggo RM, Bhaskaran K, Wong AYS, Williamson EJ, Forbes H, Wing K, McDonald HI, Bates C, Bacon S, Walker AJ, Evans D, Inglesby P, Mehrkar A, Curtis HJ, DeVito NJ, Croker R, Drysdale H, Cockburn J, Parry J, Hester F, Harper S, Douglas IJ, Tomlinson L, Evans SJW, Grieve R, Harrison D, Rowan K, Khunti K, Chaturvedi N, Smeeth L, Goldacre B; OpenSAFELY Collaborative. Ethnic differences in SARS-CoV-2 infection and COVID-19-related hospitalisation, intensive care unit admission, and death in 17 million adults in England: an observational cohort study using the OpenSAFELY platform. Lancet. 2021 May 8;397 (10286):1711-24. doi: 10.1016/S0140–6736 (21) 00634-6. Epub 2021 Apr 30. Erratum in: Lancet. 2021 May 6. PMID: 33939953; PMCID: PMC8087292.

Simpson A, Ramagopalan SV. R WE ready for reimbursement? A round up of developments in RWE relating to health technology assessment: part 2. J Comp Eff Res. 2021 Sep;10 (13):957-9. doi: 10.2217/cer-2021-0144. Epub 2021 Jun 29. PMID: 34184540.

Howlader N, Forjaz G, Mooradian MJ, Meza R, Kong CY, Cronin KA, Mariotto AB, Lowy DR, Feuer EJ. The Effect of Advances in Lung-Cancer Treatment on Population Mortality. N Engl J Med. 2020 Aug 13;383 (7):640-9. doi: 10.1056/NEJMoa1916623. PMID: 32786189; PMCID: PMC8577315.

Grinda T, Antoine A, Jacot W, Blaye C, Cottu PH, Diéras V, Dalenc F, Gonçalves A, Debled M, Patsouris A, Mouret-Reynier MA, Mailliez A, Clatot F, Levy C, Ferrero JM, Desmoulins I, Uwer L, Petit T, Jouannaud C, Lacroix-Triki M, Deluche E, Robain M, Courtinard C, Bachelot T, Brain E, Pérol D, Delaloge S. Evolution of overall survival and receipt of new therapies by subtype among 20-446 metastatic breast cancer patients in the 2008-2017 ESME cohort. ESMO Open. 2021 Jun;6 (3):100114. doi: 10.1016/j.esmoop.2021.100114. Epub 2021 Apr 23. PMID: 33895695; PMCID: PMC8095121.

Grinda T, Antoine A, Jacot W, Blaye C, Cottu PH, Diéras V, Dalenc F, Gonçalves A, Debled M, Patsouris A, Mouret-Reynier MA, Mailliez A, Clatot F, Levy C, Ferrero JM, Desmoulins I, Uwer L, Petit T, Jouannaud C, Lacroix-Triki M, Deluche E, Robain M, Courtinard C, Bachelot T, Brain E, Pérol D, Delaloge S. Evolution of overall survival and receipt of new therapies by subtype among 20 446 metastatic breast cancer patients in the 2008-2017 ESME cohort. ESMO Open. 2021 Jun;6 (3):100114. doi: 10.1016/j.esmoop.2021.100114. Epub 2021 Apr 23. PMID: 33895695; PMCID: PMC8095121.

Neyt M, Devos C, Thiry N et al. Do innovative medicines against cancer always have a real added value? Belgian Health Care Knowledge Centre, 343Cs (2021). https://doi.org/10.57598/R343CS.

Simpson A, Ramagopalan SV. R WE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment. J Comp Eff Res. 2021 Jul;10 (10):797-9. doi: 10.2217/cer-2021-0098. Epub 2021 May 6. PMID: 33955236.

Padula WV, Kreif N, Vanness DJ, Adamson B, Rueda JD, Felizzi F, Jonsson P, IJzerman MJ, Butte A, Crown W. Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force. Value Health. 2022 Jul;25 (7):1063-80. doi: 10.1016/j.jval.2022.03.022. PMID: 35779937.

Birnbaum B, Nussbaum N, Seidl-Rathkopf K et al. Model-assisted cohort selection with bias analysis for generating large-scale cohorts from the EHR for oncology research. arXiv. 2020. doi: 10.48550/arXiv.2001.09765

Athey S, Tibshirani J, Wager S. Generalized random forests. Project Euclid. 2019;47 (2):1148-78. doi: 10.1214/18-AOS1709

Kreif N, Diazordaz K, Moreno-Serra R, Mirelman A, Hidayat T, Suhrcke M. Estimating heterogeneous policy impacts using causal machine learning: a case study of health insurance reform in Indonesia. Health Serv Outcomes Res Method Springer. 2022;22 (2):192-227. doi: 10.1007/s10742-021-00259-3

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316 (22):2402-10. doi: 10.1001/jama.2016.17216. PMID: 27898976.

Raumviboonsuk P, Krause J, Chotcomwongse P, Sayres R, Raman R, Widner K, Campana BJL, Phene S, Hemarat K, Tadarati M, Silpa-Archa S, Limwattanayingyong J, Rao C, Kuruvilla O, Jung J, Tan J, Orprayoon S, Kangwanwongpaisan C, Sukumalpaiboon R, Luengchaichawang C, Fuangkaew J, Kongsap P, Chualinpha L, Saree S, Kawinpanitan S, Mitvongsa K, Lawanasakol S, Thepchatri C, Wongpichedchai L, Corrado GS, Peng L, Webster DR. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med. 2019 Apr 10;2:25. doi: 10.1038/s41746-019-0099-8. Erratum in: NPJ Digit Med. 2019 Jul 23;2:68. PMID: 31304372; PMCID: PMC6550283.

Beede E, Baylor E, Hersch F et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. Presented at: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Honolulu, Hawaii, USA (2020):1-12. doi: 10.1145/3313831.3376718

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