Harnessing Real-World Data for Regulatory Use and Applying Innovative Applications

References

• US Food and Drug Administration. Real-world evidence. Available from: https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence. Accessed July 7, 2020.
   Google Scholar
• Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence - what is it and what can it tell us? N Engl J Med. 2016;375(23):2293–2297. doi:10.1056/NEJMsb1609216
   PubMed Web of Science ®Google Scholar
• McConnell MV, Shcherbina A, Pavlovic A, et al. Feasibility of obtaining measures of lifestyle from a smartphone app: the Myheart Counts Cardiovascular Health study. JAMA Cardiol. 2017;2(1):67–76. doi:10.1001/jamacardio.2016.4395
   Web of Science ®Google Scholar
• US Congress. 21st Century Cures Act. H.R. 34. Available from: https://www.congress.gov/114/bills/hr34/BILLS-114hr34enr.pdf. Accessed July 7, 2020.
   Google Scholar
• Baumfeld Andre E, Reynolds R, Caubel P, Azoulay L, Dreyer NA. Trial designs using real-world data: the changing landscape of the regulatory approval process. Pharmacoepidemiol Drug Saf. 2019. doi:10.1002/pds.4932
   Web of Science ®Google Scholar
• Cleary M. Artificial intelligence: the key to unlocking novel real-world data? Value Outcomes Spotlight. 2019;5(2):16–19.
   Google Scholar
• Khera R, Krumholz HM. With great power comes great responsibility: big data research from the national inpatient sample. Circ Cardiovasc Qual Outcomes. 2017;10:7. doi:10.1161/CIRCOUTCOMES.117.003846
   Web of Science ®Google Scholar
• Katkade VB, Sanders KN, Zou KH. Real world data: an opportunity to supplement existing evidence for the use of long-established medicines in health care decision making. J Multidiscip Healthc. 2018;11:295–304. doi:10.2147/JMDH.S160029
   PubMed Web of Science ®Google Scholar
• Matheny M, Israni ST, Ahmed M, Whicher D, editors. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. Washington, DC: National Academy of Medicine; 2019.
   Google Scholar
• US Food and Drug Administration. PDUFA VI: fiscal years 2018-2022. Available from: https://www.fda.gov/industry/prescription-drug-user-fee-amendments/pdufa-vi-fiscal-years-20182022. Accessed July 7, 2020.
   Google Scholar
• Collins R, Bowman L, Landray M, Peto R. The magic of randomization versus the myth of real-world evidence. N Engl J Med. 2020;382(7):674–678. doi:10.1056/NEJMsb1901642
   PubMed Web of Science ®Google Scholar
• Alemayehu D, Cappeleri JC, Emir B, Zou KH, editors. Statistical Topics in Health Economics and Outcomes Research. Boca Raton, FL Chapman and Hall/CRC Press; 2017.
   Google Scholar
• Chambers RB, Zou KH, Nakazuru Y, et al. RWE for lorazepam IV regulatory approval in Japan. Presented at Joint Statistical Meeting, Denver,CO; 2019.
   Google Scholar
• Gottlieb S. Harnessing real world evidence for safety and innovation. https://www.fda.gov/news-events/speeches-fda-officials/harnessing-real-world-evidence-safety-and-innovation-11192018. Accessed July 7, 2020.
   Google Scholar
• US Food and Drug Administration. Use of real-world evidence to support regulatory decision-making for medical devices: guidance for industry and Food and Drug Administration staff. Docket no. FDA-2016-D-2153. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-real-world-evidence-support-regulatory-decision-making-medical-devices. Accessed July 7, 2020.
   Google Scholar
• US Food and Drug Administration. Use of electronic health record data in clinical investigations: guidance for industry. Docket no. FDA-2016-D-1224. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-electronic-health-record-data-clinical-investigations-guidance-industry. Accessed July 7, 2020.
   Google Scholar
• US Food and Drug Administration. Framework for FDA’s real-world evidence program. Available from: https://www.fda.gov/media/120060/download. Accessed July 7, 2020.
   Google Scholar
• US Food and Drug Administration. Submitting documents using real-world data and real-world evidence to FDA for drugs and biologics: guidance for industry. Docket no. 2019-09529. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/submitting-documents-using-real-world-data-and-real-world-evidence-fda-drugs-and-biologics-guidance. Accessed July 7, 2020.
   Google Scholar
• European Medicines Agency. Regulatory perspective on real world evidence (RWE) in scientific advice. EMA human scientific committees’ working parties with patients’ and consumers’ organisations (PCWP) and healthcare professionals’ organisations (HCPWP). Available from: https://www.ema.europa.eu/en/documents/presentation/presentation-regulatory-perspective-real-world-evidence-rwe-scientific-advice-emas-pcwp-hcpwp-joint_en.pdf. Accessed July 7, 2020.
   Google Scholar
• Center for Drug Evaluation (China). Key Considerations in Using Real-World Evidence to Support Drug Development. NMPA; 2019.
   Google Scholar
• Adding real-world evidence to a totality of evidence approach for evaluating marketed product effectiveness. Available from: https://healthpolicy.duke.edu/sites/default/files/u31/totality_of_evidence_appendix.pdf. Accessed Jly 7, 2020.
   Google Scholar
• Wedam S, Fashoyin-Aje L, Bloomquist E, et al. FDA approval summary: palbociclib for male patients with metastatic breast cancer. Clin Cancer Res. 2020;26(6):1208–1212. doi:10.1158/1078-0432.CCR-19-2580
   PubMed Web of Science ®Google Scholar
• Deloitte. Getting real with real-world evidence (RWE) 2017 RWE benchmark survey. Available from: https://www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/real-world-evidence-benchmarking-survey.html. Accessed July 7, 2020.
   Google Scholar
• Davis B, Morgan J, Shah S. The future of real-world evidence Biopharma companies focus on end-to-end, AI-driven, internally developed solutions. Available from: https://www2.deloitte.com/us/en/insights/industry/life-sciences/2018-real-world-evidence-benchmarking.html?id=gx:2sm:3tw:4RWE_LSHC18::6Life_Sciences_and_Healthcare:20180711095200:Global&linkId=54041116. Accessed July 7, 2020.
   Google Scholar
• Morgan J, Feghali K, Shah S, Miranda W. RWE focus is shifting to R&D, early investments begin to pay off. Available from: https://www2.deloitte.com/us/en/insights/industry/health-care/real-world-evidence-study.html?id=us:2sm:3li:4di_gl:5eng:6di. Accessed July 7, 2020.
   Google Scholar
• Hripcsak G, Duke JD, Shah NH, et al. Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform. 2015;216:574–578.
   PubMedGoogle Scholar
• Hripcsak G, Ryan PB, Duke JD, et al. Characterizing treatment pathways at scale using the OHDSI network. Proc Natl Acad Sci U S A. 2016;113(27):7329–7336. doi:10.1073/pnas.1510502113
   Web of Science ®Google Scholar
• Hernandez-Boussard T, Monda KL, Crespo BC, Riskin D. Real world evidence in cardiovascular medicine: ensuring data validity in electronic health record-based studies. J Am Med Inform Assoc. 2019;26(11):1189–1194. doi:10.1093/jamia/ocz119
   Web of Science ®Google Scholar
• Shaw R. The 10 best machine learning algorithms for data science beginners. Available from: https://www.dataquest.io/blog/top-10-machine-learning-algorithms-for-beginners/. Accessed July 7, 2020.
   Google Scholar
• Shah P, Kendall F, Khozin S, et al. Artificial intelligence and machine learning in clinical development: a translational perspective. NPJ Digit Med. 2019;2:69. doi:10.1038/s41746-019-0148-3
   PubMed Web of Science ®Google Scholar
• Xie H. Robotic process automation in healthcare will lead to better patient outcomes. In: Information Age. Bonhill Group Plc; 2019.
   Google Scholar
• Dietz R, Raimondo C, Kremer J, Reynolds J, Saxena R. Robotics in Claims: Transformation Through Intelligent Automation. Ernst & Young LLP; 2018.
   Google Scholar
• Association for Intelligent Information Management. Intelligent information management glossary: what is robotic process automation? Available from: https://www.aiim.org/What-is-Robotic-Process-Automation#. Accessed July 7, 2020.
   Google Scholar
• Juhn Y, Liu H. Natural language processing to advance EHR-based clinical research in allergy, asthma, and immunology. J Allergy Clin Immunol. 2020;145(2):463–469. doi:10.1016/j.jaci.2019.12.897
   Web of Science ®Google Scholar
• Lin H, Long E, Ding X, et al. Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study. PLoS Med. 2018;15(11):e1002674. doi:10.1371/journal.pmed.1002674
   PubMed Web of Science ®Google Scholar
• Kiely DG, Doyle O, Drage E, et al. Utilising artificial intelligence to determine patients at risk of a rare disease: idiopathic pulmonary arterial hypertension. Pulm Circ. 2019;9(4):2045894019890549. doi:10.1177/2045894019890549
   Web of Science ®Google Scholar
• Blaivas M, Blaivas L. Are all deep learning architectures alike for point-of-care ultrasound?: evidence from a cardiac image classification model suggests otherwise. J Ultrasound Med. 2020;39(6):1187–1194. doi:10.1002/jum.15206
   Web of Science ®Google Scholar
• Mahajan V, Venugopal VK, Murugavel M, Mahajan H. The algorithmic audit: working with vendors to validate radiology-ai algorithms-how we do it. Acad Radiol. 2020;27(1):132–135. doi:10.1016/j.acra.2019.09.009
   Web of Science ®Google Scholar
• European Commission. Communication from the Commission to the European Parliament and the Council: Data Protection Rules as a Trust-Enabler in the EU and Beyond Taking Stock. Brussels, Belgium. 2019.
   Google Scholar
• Hu K, LaFontaine P, Mandic A, et al. An assessment of real-world data, artificial intelligence, and their use in drug development. Presented at Drug Information Association; February 57, 2019; Vienna, Austria.
   Google Scholar
• American Statistical Association. Statistical Partnerships Among Academe, Industry, and Government (SPAIG) award. Available from: https://www.amstat.org/ASA/Your-Career/Awards/Statistical-Partnerships-Among-Academe-Industry-and-Government-Award.aspx. Accessed July 7, 2020.
   Google Scholar
• Rizvi S, Majumdar M, Zou KH, Ray A. Partnerships in action: advancing healthcare through collaborative science in emerging markets. Available from: https://www.cioapplications.com/cxoinsights/partnerships-in-action-advancing-healthcare-through-collaborative-science-in-emerging-markets-nid-5717.html. Accessed July 7, 2020.
   Google Scholar
• Ali R, El Shahawy O, Zou KH, Sherman S, Weitzman M, Ray A. NYU Abu Dhabi and Pfizer Inc. collaboration drives progress. Available from: https://magazine.amstat.org/blog/2020/04/01/nyu-abu-dhabi-pfizer/. Accessed July 7, 2020.
   Google Scholar
• Berger ML, Dreyer N, Anderson F, Towse A, Sedrakyan A, Normand SL. Prospective observational studies to assess comparative effectiveness: the ISPOR Good Research Practices Task Force Report. Value Health. 2012;15:217–230. doi:10.1016/j.jval.2011.12.010
   PubMed Web of Science ®Google Scholar
• Zou KH, Imperato J, Ovalle JC, Li JZ, Sethi N, Ray A. Enhanced patient-centricity: optimizing patient care through AI/ML/DL. PharmaVoice. 2020;20(6):52–54.
   Google Scholar