Volume : 11, Issue : 10, October – 2024
Title:
REAL WORLD DATA
Authors :
Tejaswini Mallepula, Mrs.J.Bharathi, Dr.K.Venu Gopal
Abstract :
Clinical evidence is increasingly coming from real-world data (RWD). RWD are generated and utilized by a wide range of stakeholders, including biopharmaceutical companies, payers, clinical researchers, providers and patients. The despite of fact that drug regulation is the most well-known application for them. We outline 21 possible applications for RWD in the field of health care in this review. We also go over significant obstacles and constraints that pertain to turning in these facts into proof.
Reduced costs, improve evidence in clinical trials, accelerate development of new drugs and medical devices, holistic optimization of the healthcare system, regulatory support, healthcare quality improvement, personalized medicine, diverse data sources, large sample sizes. Data quality issues, lack of control, missing data, regulatory challenges, time consuming data cleaning, limited granularity, potential for misuse.
Cite This Article:
Please cite this article in press Tejaswini Mallepulaet al., Real World Data..,Indo Am. J. P. Sci, 2024; 11 (10).
Number of Downloads : 10
References:
1. FDA. Framework for FDA’s Real-World Evidence Program (2018). http://www.fda.gov/media/120060/download Accessed December 5, 2019.
2. Makady A, de Boer A, Hillege H, et al. What is real-world data? A review of definitions based on literature and stakeholder interviews. Value Health. 2017;20(7):858–865.PubMed Web of Science ® Google Scholar
3. Burns L, Roux NL, Kalesnik-Orszulak R, Christian J, Hukkelhoven M, Rockhold F, et al. Real-world evidence for regulatory decision-making: guidance from around the world. Clin Ther. (2022) 44:420–37. doi: 10.1016/j.clinthera.2022.01.012, PMID: [DOI] [PubMed] [Google Scholar]
4. Luce BR, Drummond M, Jonsson B, et al. EBM, HTA, and CER: clearing the confusion. Milbank Q. 2010; 88:256–276. doi: 10.1111/j.1468-0009.2010.00598.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Berger M, et al. A framework for regulatory use of real-world evidence. http://healthpolicy.duke.edu/sites/default/files/atoms/files/rwe_white_p… Updated September 13, 2017. Accessed December 5, 2019.
6. Craig P, et al. Using natural experiments to evaluate population health interventions: new Medical Research Council guidance. J Epidemiol Community Health. 2012;66(12):1182–1186. doi: 10.1136/jech-2011-200375. – DOI – PMC – PubMed
7. FDA. FDA’s Sentinel Initiative. http://www.fda.gov/safety/fdas-sentinel-initiative Updated October 18, 2019. Accessed December 5, 2019.
8. International Coalition of Medicines Regulatory Authorities. ICMRA membership country/region and regulatory Authority’s website. (2022). Available at: https://www.icmra.info/drupal/en/participatingRegulatoryAuthorities. (Accessed May, 2022).
9. Franklin JM, Patorno E, Desai RJ, Glynn RJ, Martin D, Quinto K, et al. Emulating randomized clinical trials with nonrandomized real-world evidence studies: first results from the RCT DUPLICATE initiative. Circulation. (2021) 143:1002–13. doi: 10.1161/CIRCULATIONAHA.120.051718, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar].
10. U.S. Food and Drug Administration. PDUFA VII: Fiscal years 2023–2027. (2022). Available at: https://www.fda.gov/industry/prescription-drug-user-fee-amendments/pdufa-vii-fiscal-years-2023-2027. (Accessed September, 2022).
11. U.S. Food and Drug Administration. Framework for FDA’s real-world evidence program. (2018). Available at: https://www.fda.gov/media/120060/download. (Accessed June, 2022).
12. U.S. Food and Drug Administration. Considerations for the use of real-world data and real-world evidence to support regulatory decision-making for drug and biological products. (2021). Available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-real-world-data-and-real-world-evidence-support-regulatory-decision-making-drug. (Accessed May, 2022).
13. Government of Canada. Optimizing the use of real-world evidence to inform regulatory decision-making. (2019). Available at: https://www.canada.ca/en/health-canada/services/drugs-health-products/drug-products/announcements/optimizing-real-world-evidence-regulatory-decisions.html. (Accessed Septemeber, 2022).
14. Berger ML, Sox H, Willke RJ, et al. good practices for real-world data studies of treatment and/or comparative effectiveness: recommendations from the joint ISPOR-ISPE Special Task Force on real-world evidence in health care decision making. Pharmacoepidemiol Drug Saf. 2017;26(9):1033–1039. PubMed Web of Science ® Google Scholar
15. Flaherty KT, Le DT, Lemery S. Tissue-agnostic drug development. Am Soc Clin Oncol Educ Book. 2017; 37:222–230. PubMed Google Scholar
16. Franklin JM, Glynn RJ, Martin D, et al. Evaluating the use of nonrandomized real-world data analyses for regulatory decision making. Clin Pharmacol Ther. 2019;105(4):867–877. PubMed Web of Science ® Google Scholar
17. Makady A, Ham RT, de Boer A, et al. Policies for use of real-world data in health technology assessment (HTA): a comparative study of six HTA agencies. Value Health. 2017;20(4):520–532. Web of Science ® Google Scholar
18. Beaulieu-Jones BK, Finlayson SG, Yuan W, et al. Examining the use of real-world evidence in the regulatory process. Clin Pharmacol Ther. 2020;107(4):843–852.PubMed Web of Science ® Google Scholar
19. Di Maio M, Perrone F, Conte P. Real-world evidence in oncology: opportunities and limitations. Oncologist. 2020;25(5): e746–e52.PubMed Web of Science ® Google Scholar
20. Wu J, Wang C, Toh S, et al. Use of real-world evidence in regulatory decisions for rare diseases in the United States—current status and future directions. Pharmacoepidemiol Drug Saf. 2020;29(10):1213–1218.PubMed Web of Science ® Google Scholar
21. Dlamini Z., Francies F.Z., Hull R., Marima R. Artificial intelligence (AI) and big data in cancer and precision oncology. Comput. Struct. Biotechnol. J. 2020; 18:2300–2311. doi: 10.1016/j.csbj.2020.08.019. [DOI] [PMC free article] [PubMed] [Google Scholar].
22. Adir O., Poley M., Chen G., Froim S., Krinsky N., Shklover J., Shainsky-Roitman J., Lammers T., Schroeder A. Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine. Adv. Mater. 2020; 32:1901989. doi: 10.1002/adma.201901989. [DOI] [PMC free article] [PubMed] [Google Scholar].
23. Pregelj L., Hwang T.J., Hine D.C., Siegel E.B., Barnard R.T., Darrow J.J., Kesselheim A.S. Precision Medicines Have Faster Approvals Based on Fewer and Smaller Trials Than Other Medicines. Health Aff. 2018; 37:724–731. doi: 10.1377/hlthaff.2017.1580. [DOI] [PubMed] [Google Scholar],
24. Agrawal S., Arora S., Amiri-Kordestani L., de Claro R.A., Fashoyin-Aje L., Gormley N., Kim T., Lemery S., Mehta G.U., Scott E.C., et al. Use of Single-Arm Trials for US Food and Drug Administration Drug Approval in Oncology, 2002–2021. JAMA Oncol. 2023 9:266–272. doi: 10.1001/jamaoncol.2022.5985. [DOI] [PubMed] [Google Scholar].
25. Davis C., Naci H., Gurpinar E., Poplavska E., Pinto A., Aggarwal A. Availability of evidence of benefits on overall survival and quality of life of cancer drugs approved by European Medicines Agency: Retrospective cohort study of drug approvals 2009-13. BMJ. 2017;359: j4530. doi: 10.1136/bmj. j4530. [DOI] [PMC free article] [PubMed] [Google Scholar].
26. Sentinel Initiative. Sentinel Common Data Model. http://www.sentinelinitiative.org/sentinel/data/distributed-database-common-data-model Updated October 31, 2018. Accessed December 5, 2019.
27. Sarker A, et al. Utilizing social media data for pharmacovigilance: a review. J Biomed Inform. 2015; 54:202–212. doi: 10.1016/j.jbi.2015.02.004. [DOI] [PMC free article] [PubMed] [Google Scholar].
28. Nikfarjam A, et al. Early detection of adverse drug reactions in social health networks: a natural language processing pipeline for signal detection. JMIR Public Health Surveill. 2019;5(2):e11264. doi: 10.2196/11264. [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Lorberbaum T, et al. Coupling data mining and laboratory experiments to discover drug interactions causing QT prolongation. J Am Coll Cardiol. 2016;68(16):1756–1764. doi: 10.1016/j.jacc.2016.07.761. [DOI] [PMC free article] [PubMed] [Google Scholar].
30. Home PD, et al. Rosiglitazone evaluated for cardiovascular outcomes in oral agent combination therapy for type 2 diabetes (RECORD): a multicentre, randomised, open-label trial. Lancet. 2009;373(9681):2125–2135. doi: 10.1016/S0140-6736(09)60953-3. [DOI] [PubMed] [Google Scholar