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  • Impact of Quantitative Bias Analysis (QBA) in External Control Arms

    Impact of Quantitative Bias Analysis (QBA) in External Control Arms

    Impact of Quantitative Bias Analysis (QBA) in External Control Arms

    In recent years, external control arms (ECAs) are being increasingly used in clinical research, particularly in cases where randomized controlled trials (RCTs) are not feasible or appropriate. Real-world data (RWD) from patient registries, electronic health records (EHRs), and other observational sources are the basis of ECAs, thus offering critical reference points for single-arm studies.(1, 2) However, they can significantly impact the validity of ECAs due to the risk of unmeasured confounding and systematic biases.(3) ECAs are not randomized like RCTs, which makes them fundamentally susceptible to bias owing to the differences in patient characteristics, treatment practices, and methods of data collection.(4) To address this challenge, quantitative bias analysis (QBA) can be a beneficial methodological tool.(5)

    QBA enables researchers to systematically model and calculate the possible impact of unmeasured confounding on treatment effect estimates. Instead of solely depending on conventional sensitivity analyses or assuming that statistical adjustments for measured variables are adequate, QBA offers a systematic approach to estimate how much an unobserved confounder would require to control both treatment and outcome to justify an observed treatment effect. This quantitative perspective adds refinement and transparency to studies involving ECA, particularly in cases where the severity and direction of potential bias is assessed or bounded. The increasing realization of QBA’s applicability has persuaded its implementation in regulatory and health technology assessment (HTA) scenarios, where decision-makers need robust, reliable evidence for decisions on policy and reimbursement.(5, 6)

    The Q-BASEL study (Quantitative Bias Analysis in External Control Arms for Standardization and Evidence Level) is a crucial milestone in depicting the utility of QBA in practice. This study comprehensively employed QBA techniques across several real-world ECA cases to estimate the possible impact of unmeasured confounding on the observed treatment effects. By incorporating probabilistic bias analysis and deterministic sensitivity states, the researchers were able to measure the strength of the findings from these ECAs. The results of the Q-BASEL study emphasized that even moderate confounding could drastically change the understanding of treatment benefit, while also exhibiting robustness of observed treatment effect under reasonable bias assumptions in several cases. This strengthened the value of QBA as both a diagnostic method and a reliability enhancer for ECA-based evidence.(6)

    Notably, the Q-BASEL study encourages a mindset shift, from trying to remove all bias to explicitly measuring it and putting its effect into perspective. Embracing the inherent uncertainty of RWD empowers researchers and reviewers to make more informed decisions. The study also highlights the need for transparency in assumptions, appropriate documentation of parameter scales, and stakeholder communication, all of which are vital for QBA to significantly help in decision-making. Additionally, its impact extends beyond oncology and rare diseases, two fields with frequent adoption of ECAs, to larger treatment landscapes with rising RWD integration.(6)

    With increasing use of ECAs in regulatory and HTA decisions, QBA offers a transparent, scientific method to evaluate whether treatment effect estimates may be affected by bias rather than depicting a real clinical benefit. The Q-BASEL study shows how this can be assessed with practical and reproducible approaches. With rising expectations for transparency and methodological robustness, integrating QBA into RWE studies can potentially become standard practice.

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    References

    1. Sosinsky AZ, Parzynski CS, Casso D. The Role of External Control Arms in Drug Development and Considerations for Success. ISPOR – Value & Outcomes Spotlight. 2024; 10(4).
    2. Mishra-Kalyani PS, Amiri Kordestani L, Rivera DR, et al. External control arms in oncology: current use and future directions. Ann Oncol. 2022; 33(4):376-383.
    3. Quantifying Bias in Real-World Studies: A New Hope for RWD Acceptance or Are HTAers Gonna Hate? ISPOR. 2022. [Accessed online on 10th July 2025]. Available at: https://www.ispor.org/docs/default-source/euro2022/ispor-eu-2022—qbasel-symposium—v1.pdf?sfvrsn=d14165ea_0
    4. Khachatryan A, Read SH, Madison T. External control arms for rare diseases: building a body of supporting evidence. J Pharmacokinet Pharmacodyn. 2023; 50:501-506.
    5. Thorlund K, Duffield S, Popat S, et al. Quantitative bias analysis for external control arms using real-world data in clinical trials: a primer for clinical researchers. J Comp Eff Res. 2024; 13(3):e230147.
    6. Gupta A, Hsu G, Kent S, et al. Quantitative Bias Analysis for Single-Arm Trials With External Control Arms. JAMA Netw Open. 2025; 8(3):e252152.

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  • Outcomes Research and Real-World Evidence for Women’s Health

    Outcomes Research and Real-World Evidence for Women’s Health

    Within the broader spectrum of healthcare, women represent a significant and distinct demographic with unique health needs and outcomes, underscoring the importance of focused research in this domain. The realm of women’s health research is vast and vital, addressing conditions and diseases that predominantly or exclusively affect women, such as reproductive health issues, breast and cervical cancers, and osteoporosis. Women’s health research also sheds light on how common conditions like cardiovascular diseases and autoimmune disorders present and progress differently in women compared to men. Another compelling reason to prioritize women’s health research is the significant physiological and hormonal distinctions between women and men, which can profoundly influence disease manifestation, progression, and treatment response. For example, cardiovascular disease, the leading cause of death among women globally, often exhibits atypical symptoms in women, such as fatigue and shortness of breath, rather than the classic chest pain.[1]

    However, traditional research methods often overlook these gender differences. For instance, randomized controlled trials (RCTs) often exclude pregnant women: even though such an exclusion is justified from the foetal viewpoint, such exclusions bring in inadequacy in women’s health research. Such inadequacies highlight the necessity for gathering research insights from real-world data (RWD), thereby complementing evidence from controlled settings. Thus, outcomes research and real-world evidence (RWE) becomes an important source of research information for women’s health.[2]

    Outcomes research and RWE play a pivotal role in addressing health disparities among women, particularly in maternal care. Preapproval clinical trials typically exclude pregnant women, necessitating reliance on post-approval controlled observational studies to gather evidence on pregnancy safety essential for drug labels. Regulatory agencies increasingly recommend complementing pregnancy registries and case-control studies with pregnancy cohorts nested within healthcare utilization databases, such as national registries, electronic medical records (e.g., Clinical Practice Research Datalink), and insurance claims. The utilization of RWE has uncovered significant disparities in maternal health outcomes, fostering health equity and improving overall outcomes for women.[3-7]

    In cancer care, RWE has been instrumental in advancing treatment strategies for women. Breast cancer, the most common cancer among women, has benefited significantly from real-world studies. RWE has been known to support clinical guidelines by providing data on specific subgroups of patients not well-represented in RCTs. For example, in early-relapsing HER2+ advanced breast cancer, RWE has provided valuable insights into treatment outcomes, helping to guide clinical decision-making and refine treatment protocols. Subsequently, RWE and outcomes research have facilitated a deeper understanding of the real-world efficacy of hormone therapies and the impact of different chemotherapy regimens on diverse patient populations. This has led to more personalized treatment plans that consider the unique needs of each patient, fostering improved communication between physicians and patients and enhancing overall care and outcomes for women with breast cancer.[7-9]

    Another critical facet of women’s health research involves the inclusion of all age groups, from adolescence to post-menopause, each life stage presenting unique health challenges. RWE serves as a cornerstone in shaping interventions tailored to these diverse needs. For instance, a 2022 study revealed the influence of social media on the mental health of young girls, emphasizing the need for interventions promoting positive body image. Conversely, for older women, real-world data has yielded insights into treatment effectiveness and patient-reported outcomes, guiding healthcare strategies to address age-specific health concerns.[9-12]

    In conclusion, the outcomes research and RWE are indispensable for advancing women’s health. By illuminating the intricacies of disease presentation, treatment outcomes, and healthcare disparities, these methodologies empower healthcare practitioners to deliver more personalized, effective, and equitable care to women across diverse demographics and life stages. From addressing cardiovascular disease manifestations to improving maternal care and refining breast cancer treatment strategies, outcomes research and RWE play a pivotal role in ensuring that healthcare solutions are truly reflective of and responsive to the needs of all women, from adolescence to post-menopause. Through these approaches, we continue to break barriers, promote health equity, and enhance the quality of care for women worldwide.

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    References:

    1. Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence—what is it and what can it tell us. 2016 Dec 8;375(23):2293-7.
    2. Siristatidis C, Karageorgiou V, Vogiatzi P. Current Issues on Research Conducted to Improve Women’s Health. Healthcare (Basel). 2021 Jan 17;9(1):92. doi: 10.3390/healthcare9010092.
    3. Why we know so little about women’s health. Available from: https://www.aamc.org/news/why-we-know-so-little-about-women-s-health.
    4. Heyrana K, Byers HM, Stratton P. Increasing the participation of pregnant women in clinical trials. Jama. 2018 Nov 27;320(20):2077-8.
    5. Gliklich RE, Dreyer NA, Leavy MB, editors. Registries for evaluating patient outcomes: a user’s guide.
    6. Food and Drug Administration. Guidance for industry: establishing pregnancy exposure registries. Washington, DC: US Department of Health and Human Services. Available from: https://www.fda.gov/media/75607
    7. Mitchell AA. Systematic identification of drugs that cause birth defects—a new opportunity. New England Journal of Medicine. 2003 Dec 25;349(26):2556-9.
    8. Huybrechts KF, Bateman BT, Hernández‐Díaz S. Use of real‐world evidence from healthcare utilization data to evaluate drug safety during pregnancy. Pharmacoepidemiology and drug safety. 2019 Jul;28(7):906-22.
    9. Schettini F, Conte B, Buono G, et al. T-DM1 versus pertuzumab, trastuzumab and a taxane as first-line therapy of early-relapsed HER2-positive metastatic breast cancer: an Italian multicenter observational study. ESMO open. 2021 Apr 1;6(2):100099.
    10. DuBenske LL, Schrager SB, Hitchcock ME, et alKey elements of mammography shared decision-making: a scoping review of the literature. Journal of General Internal Medicine. 2018 Oct;33:1805-14.
    11. Papageorgiou A, Fisher C, Cross D. Why don’t I look like her? How adolescent girls view social media and its connection to body image. BMC women’s health. 2022 Jun 27;22(1):261.
    12. Maruszczyk K, Aiyegbusi OL, Torlinska B, et al. Systematic review of guidance for the collection and use of patient-reported outcomes in real-world evidence generation to support regulation, reimbursement and health policy. Journal of Patient-Reported Outcomes. 2022 Jun 2;6(1):57.

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  • Challenges in the Use of Real-World Evidence for Pharmacoeconomic Modeling

    Challenges in the Use of Real-World Evidence for Pharmacoeconomic Modeling
    The image mentions "Challenges in the Use of Real-World Evidence for Pharmacoeconomic Modeling" which is the topic of the blog

    Pharmacoeconomic modeling is vital for healthcare decision-making, enabling stakeholders to evaluate the value and cost-effectiveness of pharmaceutical interventions. These models offer insights into clinical and economic outcomes, aiding policymakers, providers, and payers in informed resource allocation and reimbursement decisions. (1)

    RCTs have long been regarded as the gold standard for evaluating the efficacy and safety of pharmaceutical interventions. These studies are carefully designed, with strict inclusion and exclusion criteria, randomization procedures, and blinding methods to minimize bias. RCTs provide robust evidence regarding the clinical effectiveness of a treatment, allowing for direct comparisons between the intervention and control groups. This high level of internal validity makes RCTs an essential component of pharmacoeconomic modeling, as they form the basis for estimating treatment effects and health outcomes. (2, 3)

    However, RCTs also have their limitations. They often involve a select patient population that may not fully represent the broader range of patients encountered in real-world clinical practice. Additionally, RCTs are typically conducted under controlled conditions, which may not reflect the complexities and variations of routine clinical care. This is where real-world evidence comes into play. The use of real-world evidence (RWE) is gaining momentum as a complementary data source to enhance the accuracy and generalizability of these models.

    The integration of RWE into pharmacoeconomic modeling can address some of the limitations of RCTs and enhance the generalizability and external validity of the models. RWE offers a broader perspective by including patients with comorbidities, variations in treatment adherence, and diverse healthcare settings. It captures the real-world complexities that influence treatment outcomes, such as variations in healthcare utilization patterns and patient characteristics. By complementing RCT data with real-world evidence, pharmacoeconomic models can provide a more comprehensive understanding of treatment effectiveness, safety, and cost outcomes in a broader patient population. (4)

    However, the utilization of RWE in pharmacoeconomic modeling is not without its challenges. One of the primary challenges is the inherent heterogeneity and variability of real-world data sources. Unlike RCTs, which follow a standardized protocol, real-world data is derived from diverse sources with varying data collection methods, patient populations, and treatment patterns. This heterogeneity can introduce bias and confounding factors that need to be carefully considered during the modeling process. To address this challenge, researchers must ensure that the data used for modeling is representative of the target population and adequately address potential sources of bias through careful study design and statistical methods. (5)

    The completeness and quality of real-world data pose another challenge. Unlike RCTs, where data collection is planned and executed with a specific research objective in mind, real-world data is often collected for clinical or administrative purposes. This can result in missing or incomplete data, inconsistent documentation practices, and other data quality issues. Researchers must invest considerable effort in data cleaning, validation, and standardization to ensure the reliability and accuracy of the data used for modeling. Collaborations with data partners, data curation initiatives, and the use of data validation techniques can help address these challenges and improve the quality of real-world evidence. (5)

    The temporal aspect of real-world data also poses challenges for pharmacoeconomic modeling. RCTs typically follow a predefined study protocol with specific endpoints and follow-up periods. In contrast, real-world data is collected longitudinally, often spanning different time periods and healthcare settings. This variability in data collection timeframes can impact the accuracy and validity of modeling outcomes, particularly when assessing long-term effectiveness, safety, and cost outcomes. Researchers must carefully account for the timing and duration of data collection and any temporal trends or changes in treatment patterns that may influence the outcomes of interest. (5)

    Another critical challenge is the generalizability of real-world evidence to broader populations and settings. RCTs are often conducted in controlled environments with carefully selected patient populations, which may not fully represent the diverse patient characteristics and treatment patterns encountered in routine clinical practice. Real-world data, on the other hand, offers the advantage of capturing a broader patient population. However, generalizing findings from real-world studies to different patient populations or healthcare systems requires careful consideration and potentially the use of additional statistical methods or validation studies. (5)

    To address the above-mentioned challenges, several approaches can be adopted. Standardizing methods for data collection and analysis ensures consistency and enhances reliability. Establishing data quality standards improves the trustworthiness of RWE. Expanding data sources to include electronic health records and patient-generated data enhances the representation of diverse populations and conditions. Addressing privacy concerns through proper data governance safeguards confidentiality. Building capacity through training programs enhances researchers’ skills in utilizing RWE. Fostering collaboration among stakeholders facilitates the effective utilization of RWE in decision-making processes. By implementing these solutions, the reliability and generalizability of RWE can be improved, leading to more informed pharmacoeconomic analyses and evidence-based decision-making. (5)

    In conclusion, the integration of real-world evidence into pharmacoeconomic modeling holds great potential for enhancing the accuracy and generalizability of these models. However, challenges related to data heterogeneity, data quality, temporal aspects, and generalizability must be carefully addressed. By investing in rigorous study design, data curation, and validation processes, researchers can overcome these challenges and derive meaningful insights from real-world evidence, ultimately leading to more robust and informed healthcare decision-making.

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    References:

    1. Roberts MH, Ferguson GT. Real-World Evidence: Bridging Gaps in Evidence to Guide Payer Decisions. Pharmacoecon Open. 2021 Mar;5(1):3-11.
    2. Cartwright N. Are RCTs the gold standard?. BioSocieties. 2007 Mar;2(1):11-20.
    3. Grimberg F, Asprion PM, Schneider B, et al. The real-world data challenges radar: a review on the challenges and risks regarding the use of real-world data. Digital Biomarkers. 2021;5(2):148-57
    4. Eichler HG, Pignatti F, Schwarzer‐Daum B, et al. Randomized controlled trials versus real world evidence: neither magic nor myth. Clinical Pharmacology & Therapeutics. 2021 May;109(5):1212-8.
    5. Hampson G, Towse A, Dreitlein WB, Henshall C, Pearson SD. Real-world evidence for coverage decisions: opportunities and challenges. Journal of comparative effectiveness research. 2018 Dec;7(12):1133-43.

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  • External Control Arms (ECA): Optimizing Submissions to HTA Agencies

    External Control Arms (ECA): Optimizing Submissions to HTA Agencies
    External Control Arms (ECA): Optimizing Submissions to HTA Agencies

    In the realm of pharmaceutical development, the pursuit of safe and effective treatments is paramount. To achieve this, rigorous clinical trials are conducted to evaluate the efficacy and safety of new interventions. However, the traditional randomized controlled trial (RCT) framework, while invaluable, is not always feasible or ethical. In such cases, External Control Arms (ECAs) have emerged as a potential solution.(1)

    ECA refers to a trial design strategy where an experimental treatment arm is compared to a control group using external data rather than traditional randomization. This approach is particularly useful when randomization is impractical, such as in rare diseases or when the experimental treatment is intended for patients with no other viable options. ECAs utilize historical, observational, or real-world data to construct an appropriate control arm against which the experimental treatment can be evaluated. (2,3)

    Creating an ECA involves several steps. First, a suitable data source is identified, which could include medical registries, electronic health records, or administrative databases. The control group is then defined using patient-level data from the chosen source. Statistical methods, such as propensity score matching or weighting, are often employed to balance the characteristics of the treatment and control groups, reducing bias and ensuring comparability.(4)

    ECAs offer several advantages over traditional RCTs. They can expedite the evaluation process by providing more timely results, potentially accelerating patient access to promising therapies. ECAs can also be more cost-effective, as they utilize existing data rather than requiring the recruitment and monitoring of additional participants. Furthermore, they can fill the evidence gap in rare diseases, where conducting RCTs may be challenging due to limited patient populations. ECAs offer an opportunity to evaluate interventions in such cases, providing valuable insights for patients and healthcare providers.(1, 3)

    However, ECAs also have their limitations. As they rely on non-randomized data, there is an inherent risk of confounding and bias. While statistical methods can mitigate some of these concerns, residual confounding remains a potential issue. Additionally, the use of ECAs requires careful consideration and discussion with regulatory bodies, as there may be skepticism regarding the reliability and generalizability of the external data. Striking a balance between the benefits and limitations of ECAs is crucial to ensure their appropriate and ethical application.(2)

    Optimizing ECAs for submission to HTA agencies involves careful planning and execution. First and foremost, robust data sources with high-quality information are essential. This may involve collaborations with data custodians, healthcare institutions, or research networks to access relevant datasets. Additionally, comprehensive data analysis plans should be developed, specifying the statistical methods to be employed, addressing potential biases, and ensuring transparency in reporting results.(1, 2)

    Collaboration and engagement with HTA agencies throughout the ECA process is crucial. Early dialogue can help align expectations, address methodological concerns, and understand the specific requirements of the agency. Transparent reporting of data sources, study limitations, and potential biases is essential for successful submission. It is imperative to demonstrate the validity and reliability of the ECA results and their relevance to the target population.(5)

    In the current landscape, regulations and guidelines regarding ECAs for HTA submissions are still evolving. HTA agencies are increasingly recognizing the value of ECAs as an alternative to traditional RCTs, particularly in situations where randomization is not feasible. As the field progresses, it is expected that guidelines and frameworks will continue to be refined to ensure standardized and rigorous evaluation of ECAs.(2, 5)

    In conclusion, External Control Arms (ECAs) provide a valuable alternative to traditional randomized controlled trials, particularly in cases where randomization is not feasible or ethical. Their ability to expedite the evaluation process, fill evidence gaps in rare diseases, and potentially reduce costs make them an appealing option. However, careful consideration of potential biases and engagement with HTA agencies are crucial to ensure the appropriate use and optimization of ECAs for submissions. As the field continues to evolve, collaboration and transparent reporting will be key to harnessing the full potential of ECAs in advancing pharmaceutical research and development.

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    Reference

    1. Davi R, Mahendraratnam N, Chatterjee A, et al. Informing single-arm clinical trials with external controls. Nat Rev Drug Discov. 2020 Dec;19(12):821-822.
    2. Burcu M, Dreyer NA, Franklin JM, et al. Real-world evidence to support regulatory decision-making for medicines: Considerations for external control arms. Pharmacoepidemiol Drug Saf. 2020 Oct;29(10):1228-1235.
    3. Ventz S, Lai A, Cloughesy TF, et al. Design and Evaluation of an External Control Arm Using Prior Clinical Trials and Real-World Data. Clin Cancer Res. 2019 Aug 15;25(16):4993-5001.
    4. Seeger JD, Davis KJ, Iannacone MR, et al. Methods for external control groups for single arm trials or long-term uncontrolled extensions to randomized clinical trials. Pharmacoepidemiol Drug Saf. 2020 Nov;29(11):1382-1392.
    5. Curtis LH, Sola-Morales O, Heidt J, et al. Regulatory and HTA Considerations for Development of Real-World Data Derived External Controls. Clin Pharmacol Ther. 2023 Apr 20.

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  • Generating Clinical Evidence From Electronic Health Records and Patient Registries

    Generating Clinical Evidence From Electronic Health Records and Patient Registries

    Both electronic health records (EHRs) and patient registries store and use patient-related clinical information. However, they are conceptualized for different purposes. Both are a significant source of real-world evidence (RWE) as they gather a considerable amount of clinical information collected in the real-world setting.

    An EHR is an electronic record of health data generated during routine patient care delivered by healthcare providers.(1) EHRs are widely used to obtain information on several medical parameters from patients and maintain clinical workflows.(2) They usually comprise data on patients’ demographic, vitals, administrative, claims (medical and pharmacy), and clinical parameters. They also include other patient-related information, such as data from health-related quality-of-life instruments, home-monitoring devices, and caregiver assessments.(3)

    EHRs usually represent individual care structures, such as primary, emergency, and intensive care units, and are visit-oriented and transferable. They also offer data from integrated systems in single or linked hospitals.(4) As the use of EHRs becomes extensive in clinical research, it is only ideal that they are designed to enhance diagnosis and clinical care to improve their relevance further. The design of EHRs can also update with time as the technology advances or depending on external factors, such as changes in data type as per coding or reimbursement patterns.(1, 3)

    A patient registry is “an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure and that serves one or more predetermined scientific, clinical, or policy purposes.”(1) Patient registries are crucial in research as an ultimate platform for focused information about patients with specific health conditions. They often also help answer questions otherwise not answered by randomized clinical trials (RCTs), owing to practicality or ethicality. Registry data also help reduce the time and cost of prospective data collection. RWD generated in registries enables hypotheses generation in research, thus helping descriptive studies and research in health services.(5) Registries are typically patient-oriented and goal-driven. They are designed to collate information on specific exposures and health outcomes. Patient registries can be population-based or hospital-based.

    Often data captured from EHRs are used to construct patient registries. Specifically, EHRs can facilitate certain functions for patient registries, such as collection, cleaning, and storage of data. Likewise, a registry can enhance the value of the information gathered in the EHRs, for instance, comparative effectiveness, safety, and value, population management, and quality reporting, among others.(6)

    Data from EHRs, either as stand-alone or as complementary information to the primary research or data from administrative databases, have been used to support observational studies. For instance, the Euro Heart Survey (7), the Eurobservational Research Program (EORP) which followed the survey,(8) and the AHA Get With the Guidelines (AHA GWTG) (9) show clinical information from EHRs on several cardiovascular diseases.(3) Moreover, the EU-ADR project connects eight databases from four European countries (United Kingdom, Italy, The Netherlands, and Denmark) to facilitate the analysis of specific target adverse drug reactions (ADRs).(10) The USFDA uses data from EHRs from various sources, including sentinel systems,(11) claims databases (Medicare and Medicaid Services), and Veterans Affairs, among others, to support safety investigations for products after marketing approvals.(12)

    Many clinical registries across the globe comprise patient data on acute and chronic stages of different diseases, such as cancer, cystic fibrosis, and multiple sclerosis, to name a few. For instance, countries like the US, Canada, Australia, Germany, Sweden, and Argentina, have registries to monitor and store patient data on acute stroke. The Cystic Fibrosis Foundation Patient Registry is a clinical quality registry (CQR), developed from an epidemiological and clinical research model.(13) The American Heart Association (AHA) recommends 5 key concepts in establishing patient registries: ensuring high quality data, linking registries with relevant supplemental data, integrating registries with EHRs, safeguarding privacy, and funding considerations.(14) CQRs have also been reported to contain extensive clinical information that can complement data from government-monitored registries. These data are vital for assessing the quality of care and research.(5, 15)

    Clinical data from both EHRs and registries can generate meaningful evidence to enhance trial efficiency and optimize novel research approaches. These RWD sources can help comparative effectiveness research while facilitating new trial designs to address unmet clinical needs. Their use seems hopeful. However, the technological advancements in these sources need to be looked at with applicable care measures to ensure data privacy and confidentiality.

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    References

    1. Ehrenstein V, Kharrazi H, Lehmann H, et al. Obtaining Data From Electronic Health Records. In: Gliklich RE, Leavy MB, Dreyer NA, editors. Tools and Technologies for Registry Interoperability, Registries for Evaluating Patient Outcomes: A User’s Guide, 3rd Edition, Addendum 2 [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2019 Oct. Chapter 4. Available from: https://www.ncbi.nlm.nih.gov/books/NBK551878/
    2. Gliklich R, Dreyer N, Leavy M, eds. Registries for Evaluating Patient Outcomes: A User’s Guide. Third edition. Two volumes. (Prepared by the Outcome DEcIDE Center [Outcome Sciences, Inc., a Quintiles company] under Contract No. 290 2005 00351 TO7.) AHRQ Publication No. 13(14)-EHC111. Rockville, MD: Agency for Healthcare Research and Quality. April 2014. Available from: http://www​.effectivehealthcare.ahrq.gov
    3. Cowie MR, Blomster JI, Curtis LH, et al. Electronic health records to facilitate clinical research. Clin Res Cardiol. 2017; 106(1):1-9.
    4. Hayrinen K, Saranto K, Nykanen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform. 2008; 77:291–304.
    5. Hoque DME, Kumari V, Hoque M, et al. Impact of clinical registries on quality of patient care and clinical outcomes: A systematic review. PLOS ONE. 2017; 12(9): e0183667.
    6. Gliklich RE, Dreyer NA, Leavy MB. Interfacing Registries With Electronic Health Records. Registries for Evaluating Patient Outcomes: A User’s Guide. 2. Third ed. Rockville, MD: Agency for Healthcare Research and Quality (AHRQ); 2014. p. 3–22.
    7. Scholte op Reimer W, Gitt A, et al. Cardiovascular diseases in Europe. Euro Heart Survey−2006. European Society of Cardiology, 2006.
    8. Ferrari R. EURObservational research programme. Eur Heart J. 2010; 31:1023–1031.
    9. Smaha LA. The American Heart Association Get with the Guidelines program. Am Heart J. 2004; 148:S46–S48.
    10. Trifiro G, Fourrier-Reglat A, Sturkenboom MC, et al. The EU-ADR project: preliminary results and perspective. Stud Health Technol Inform. 2009; 148:43–49.
    11. Ball R, Robb M, Anderson SA, Dal Pan G. The FDA’s sentinel initiative-A comprehensive approach to medical product surveillance. Clin Pharmacol Ther. 2016; 99:265–268.
    12. Staffa JA, Dal Pan GJ. Regulatory innovation in postmarketing risk assessment and management. Clin Pharmacol Ther. 2012; 91:555–557.
    13. Schechter MS, Fink AK, Homa K, Goss CH. The Cystic Fibrosis Foundation Patient Registry as a tool for use in quality improvement. BMJ quality & safety. 2014; 23(Suppl 1):i9–i14.
    14. Bufalino VJ, Masoudi FA, Stranne SK, et al. The American Heart Association’s recommendations for expanding the applications of existing and future clinical registries a policy statement from the American Heart Association. Circulation. 2011; 123(19):2167–79.
    15. Emilsson L, Lindahl B, Koster M, et al. Review of 103 Swedish Healthcare Quality Registries. Journal of Internal Medicine. 2015; 277(1):94–136.

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  • Comparative Effectiveness in Real-World Settings through Pragmatic Clinical Trials

    Comparative Effectiveness in Real-World Settings through Pragmatic Clinical Trials

    Randomized controlled trials (RCTs) are the mainstay of clinical research; it is estimated that about 18,000 RCTs are published each year. However, traditional RCTs usually take a long time to complete, are expensive, and the results are challenging to generalize to the real-world since they are derived under ideal conditions with strict inclusion and exclusion criteria. This brings in an additional layer of complexity for decision-making by the healthcare stakeholders and reimbursement authorities. With an intention to resolve this challenge, fuelled by the increasing global shift towards personalized medicine and value-based payment models, new methods for generating efficacy and safety evidence of interventions in real-world settings are continuously sought after.[1,2]

    This has been a major driver for a rapid increase in interest in comparative effectiveness research (CER), which aims to compare the benefits, risks, and sometimes costs of alternative healthcare interventions (medicines, medical devices, procedures and health services) in real-world settings. CER aims to assist consumers, clinicians, purchasers, and policymakers in making informed decisions, thereby improving healthcare at both the individual and population levels.[3]

    CER was brought into spotlight with the introduction of the American Recovery and Reinvestment Act in 2009 which provided support of $1.1 US billion over 2 years for conducting CER. This stimulated an increase in the observational studies in the short run and conducting RCTs in the long run.[4] In 2020, the Patient-Centered Outcomes Research Institute (PCORI) had invested nearly $2.6 billion in more than 700 patient-centered CER studies in the USA.[5] In 2011, it was estimated that CER may contribute to a $31.6 billion reduction in research and development costs over a 10 year period by improving market access and reimbursement from private insurers.[6]

    CER involves non-inferiority trials between two interventions having similar therapeutic effects differing in other aspects relevant to stakeholders like costs, adverse effect profile, and route of administration. Among several trial designs, key trial design proposed in CER is Pragmatic Clinical Trials (PCT). PCTs are in fact RCTs, conducted in a real-world setting: the evidence generated through PCTs can be translated into patient care more efficiently and with a better generalizability. While traditional RCTs use a placebo or well-controlled alternative intervention in a tightly controlled study setting, PCTs are intended to maintain the internal validity of RCTs and maximize the external validity (generalizability and applicability). PCTs are designed and implemented in ways that would better address the demand for evidence about real-world risks, and benefits for informing clinical and health policy decisions.[4,7]

    PCTs are gaining traction in CER due to their potential to efficiently generate evidence to inform real-world health care decisions by embedding research into routine care with the goals of implementation research. A notable example of CER through PCTs is the ALLHAT trial reported in 2004, which concluded that thiazides are as effective as ACE inhibitors in the management of Hypertension.[8] Similarly, the 2006 CATIE trial reported that atypical antipsychotics are ineffective compared to placebo in elderly patients with dementia.[8]

    Despite its advantages, the “embedded” nature of PCTs (i.e RCTs embedded in real-world setting) faces ethical and regulatory challenges. Existing GCP guidelines intended for traditional RCTs are insufficient in the areas of PCTs, and appropriate reforms are needed that are relevant to conduct PCTs.[9] The design and quality of a CER depend on the proper choice of the non-inferiority margin. However, defining the non-inferiority margin can be complex and quite challenging. Attrition bias adds to these complexities. These concerns are being sorted out with the evidence from previous studies, preliminary data, and/or clinical judgment that are very helpful in allowing the trialists to make reasonable assumptions about an anticipated effect of the reference treatment.[10,11]

    Challenges also exist in sustaining the behavioural change following decisions from CER. For example, based on CER, a clinical decision was made not to use stents for stable angina: this reduced stent implants by 13% in the US for 4 years; however, by 2009, the number of implants returned back to previous levels.[12]

    Development of proper regulatory standards can enable the realization of the full potential of CER conducted through pragmatic trials to fill the research-practice gap in healthcare decision-making, reduce variability in clinical practice, and determine the high-quality care for all patients. Indeed, CER has the potential to provide the best possible treatment choices to the patients and the healthcare providers.

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    References:

    1. https://www.asianhhm.com/healthcare-management/decision-based-evidence-making
    2. Alsop J et al. The mixed randomized trial: combining randomized, pragmatic and observational clinical trial designs. Journal of Comparative Effectiveness Research. 2016;5(6):569-579.
    3. Dang A, Kaur K. Comparative effectiveness research and its utility in In-clinic practice. Perspectives in Clinical Research. 2016;7(1):9.
    4. Mullins C et al. Generating Evidence for Comparative Effectiveness Research Using More Pragmatic Randomized Controlled Trials. PharmacoEconomics. 2010;28(10):969-976.
    5. https://www.pcori.org/news-release/pcori-board-approves-new-150-million-initiative-fund-large-scale-patient-centered-clinical-studies. 2020.
    6. https://www.kff.org/wp-content/uploads/sites/3/2011/05/cer_paper_final.pdf
    7. Chalkidou K et al. The role for pragmatic randomized controlled trials (pRCTs) in comparative effectiveness research. Clinical Trials. 2012;9(4):436-446.
    8. Schneeweiss S. Developments in Post-marketing Comparative Effectiveness Research. Clinical Pharmacology and Therapeutics. 2007;82(2):143-156.
    9. Mentz R et al. Good Clinical Practice Guidance and Pragmatic Clinical Trials. Circulation. 2016;133(9):872-880.
    10. Colditz G, Winter A. Clinical trial design in the era of comparative effectiveness research. Open Access Journal of Clinical Trials. 2014;:101.
    11. Siegel J et al. Comparative Effectiveness Research in the Regulatory Setting. Pharmaceutical Medicine. 2012;26(1):5-11.
    12. Kupersmith J, Ommaya A. The Past, Present, and Future of Comparative Effectiveness Research in the US Department of Veterans Affairs. The American Journal of Medicine. 2010;123(12):e3-e7.

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  • RWE Framework by NICE (UK) for Optimizing RWD Collection and RWE Generation

    RWE Framework by NICE (UK) for Optimizing RWD Collection and RWE Generation

    Evidence from randomized clinical trials (RCTs) continues to be the standard reference point for treatment efficacy across the world. However, RCTs enrol patients based on strict inclusion and exclusion criteria, and hence RCT evidence is often not generalizable and inadequate for contributing to the day-to-day clinical practice decisions. Consequently, researchers are more interested in using real-world data (RWD) to guide healthcare decisions.(1) Analysis of RWD that enables risk vs. benefit assessment while also providing data on the utility of medical intervention is called the real-world evidence (RWE).(2)

    With the growing interest in adopting RWE for decision-making, regulators are formally introducing recommendations for both collecting RWD and generating RWE. The latest is the National Institute of Health Care and Excellence (NICE, UK). In June 2022, NICE launched a real-world evidence (RWE) framework for optimizing RWD collection to fill the knowledge gaps and make innovative healthcare interventions easily accessible to patients.(3)

    The NICE defines RWD as data on patient healthcare delivery gathered from real-world sources not controlled by any eligibility criteria like those in RCTs. The NICE guidance suggests that RWD and RWE can be used for various purposes, such as to distinguish diseases, interventions, and patient outcomes, to design and endorse economic models, to validate digital health applications (cases of RWD used to develop clinical algorithms), to address health inequalities, and to assess the impact of interventions on care delivery, among others.(3)

    The NICE RWE Framework is a part of NICE Strategy 2021 to 2026,(4) a five-year strategic plan focusing on the RWE use to fill evidence gaps, enhance NICE’s decision making, and facilitate patient access to innovative health technologies. The Framework will help identify the need to use RWE to limit uncertainties and advance guidance. The Framework thoroughly defines the best practices for designing, conducting, and reporting RWE studies to improve the quality and transparency of the evidence.(3)

    The NICE RWE Framework outlines the role of RWE in health technology assessments (HTAs). It has a separate dedicated section on best practices for comparative effectiveness studies as they have more refined considerations requiring higher precision. The Framework also offers the Data Suitability Assessment Tool (DataSAT) for assessing the data’s relevance and validity for the intended purpose. The goal of DataSAT is to compel researchers to justify the choice of the RWD source. Like other frameworks provided by FDA and EMA, the DataSAT focuses on the origin, quality, and reliability of data. The Framework also recommends that researchers follow the ‘target trial’ approach while designing an RWE study to pursue all measures to limit bias, control the confounders, and assess the robustness of the findings.(5)

    Although the NICE RWE Framework is at par with the other guidance documents by the FDA and EMA, it still has some gaps. For instance, it does not define the minimal criteria for acceptance of RWE study elements. Also, not all the RWE study elements are mentioned; therefore, more detailed processes and checklists are required for more comprehensive guidance.(5)

    It is expected that the NICE RWE Framework will keep updating with evolving processes and methodologies. In addition to the existing frameworks by other regulators such as the USFDA,(2) the NICE RWE framework will be an excellent resource for developing RWE studies, particularly comparative effectiveness studies. NICE encourages the submission of RWE studies with an open dialogue with the pharmaceutical companies. Transparency and accountability are central components of the RWE Framework as it enables researchers to provide all the possible documentation and justification of the RWE study design and execution.(5)

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    References

    1. McDonald L, Lambrelli D, Wasiak R, Ramagopalan SV. Real-world data in the United Kingdom: opportunities and challenges. BMC Med. 2016; 14(1):97.
    2. Framework for FDA’s Real-World Evidence Program. December 2018. Available at: https://www.fda.gov/media/120060/download
    3. The National Institute of Health and Care Excellence Real World Evidence Framework. June 23, 2022. Available at: https://www.nice.org.uk/corporate/ecd9/resources/nice-realworld-evidence-framework-pdf-1124020816837
    4. The NICE Strategy 2021 to 2026. Available at: https://www.nice.org.uk/about/who-we-are/corporate-publications/the-nice-strategy-2021-to-2026
    5. Jaksa A. RWE Guidance: NICE’s RWE Framework. April 2022. Available at: https://aetion.com/evidence-hub/rwe-guidance-nices-rwe-framework/

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  • The Expanding Role of Real-World Evidence in Evaluation of Medical Devices

    The Expanding Role of Real-World Evidence in Evaluation of Medical Devices

    Concerns about the consistency of the post-marketing surveillance (PMS) for safety of medical devices is well known across the world. Only around 13% of post-marketing surveillance (PMS) clinical studies are completed on medical devices.[1] This is because new products or line extensions are launched frequently, even before full clinical trials of the parent device are conducted. This forces the manufacturers to reconsider whether a clinical study is worth funding, especially if the study timeline may result in publications reporting results on a previous generation’s technology. Furthermore, demonstration of the effectiveness of a device in clinical trials is challenging since the outcomes depend highly upon the clinician’s training and healthcare settings. In addition to this, continual and rapid changes in device design, and challenges pertaining to the use of placebo and blinding techniques, contribute to the complexity in conducting medical device trials.[2]

    Another outcome of these challenges with medical device trials is the difficulty in conducting health technology assessments (HTA) of medical devices. Most healthcare systems across the globe have implemented value analysis mechanisms to assess the clinical and economic impact of medical devices to inform policy decisions. In line with this, HTAs of medical devices are increasingly required to ensure efficacy, and safety, and also to support funding, coverage, and reimbursement decisions or price negotiations. However, the quantity of clinical evidence generated through randomized controlled trials (RCTs) of medical devices is often less, making HTA difficult.[3,4]

    These challenges with respect to the availability of clinical evidence resulted in an unmet need among device manufacturers to invest in collecting evidence from other sources. And one possible solution to these problems is in the form of harnessing the power of real-world evidence (RWD).

    With advances in technologies, modern medical devices (especially wearables) are largely connective: this means that these devices have an inherent capacity to generate RWD enabling translation to real world evidence (RWE). RWE has already been used for safety assessment in the form of PMS studies of medical devices. In addition, it has been increasingly realized that RWE may also be used to support medical device development. These include RWE used as external control of a single test group, clinical evaluations leading to modification of clinical value and registrations of the device, humanitarian device exemptions (HDE), premarket approval applications, support device reclassification petitions, and expanded labeling claims. Also, RWE on medical devices is useful in studying disease epidemiology, validating biomarkers, and refining treatment patterns. RWE can assist in surveillance and early identification of device design issues or opportunities for improvements or product extensions. RWE has the potential to reveal failure modes that are not previously diagnosed in the pre-clinical analysis thereby motivating testing advancements.[5]

    Examples of using RWE in medical device development include using existing RWD of the control device during a prospective trial for a novel device; indication expansion of drug-eluting stents; some USFDA approvals, such as those of scoliosis devices, vertebral body tethering devices, the Sapien 3 device for transcatheter aortic valve replacement (TAVR); the report on incidence rates of Microbial keratitis in pediatric contact lens users, and so on.[6]

    Realising the importance of RWE in medical devices, the USFDA developed the National Evaluation System for health Technology (NEST) in 2016, with a mission to accelerate and translate new and safe health technologies leveraging RWE throughout the lifecycle of the medical device, thereby optimizing device healthcare. NEST currently consists of 12 network collaborators representing more than 195 hospitals and 3,942 outpatient clinics, responds to the research questions of stakeholders, including medical device manufacturers, and generates crucial RWE. In 2017, the Center for Devices and Radiological Health (CDRH) of the FDA issued a guidance document on the use of RWE in supporting regulatory decisions for medical devices. In 2021, the FDA published 90 examples of regulatory submissions on medical devices using RWE from 2012 to 2019 showcasing the growing role of RWE in medical devices.[6]

    In the UK, the ratification of the Medical Device Regulation MDR 2017/745 occurred in 2021, which requires Post-Market Clinical Follow-up (PMCF) of all medical devices continually and throughout the entire lifetime of the device that indirectly contributes to the robust data that are utilized for product advancements.[7] The PMCF uses RWE to a large extent.

    There are some challenges for the adoption of RWE in medical devices, the main ones being the data quality, lack of standard endpoints in data collection, and inability to assess the incremental value of the devices when multiple medical devices are used during a single procedure. To strengthen the RWD on devices, Unique Device Identifiers (UDIs) have been mandated by the FDA for all devices, simplifying the tracking of device impacts, thereby creating a robust RWD. Modern medical devices also contribute to robust RWE generation by reducing the site visits, increasing the cohorts through Federal data networking, and reducing costs & timelines in medical product development.[6,8]

    Unlike drugs, the use of RWE in medical devices is in the embryonic stage and has the potential to boom in the coming years. With the complexities of RCTs of medical devices and the evolution of the regulatory process, making evidence requirements by regulatory bodies worldwide, manufacturers are paying more attention to their evidence generation plans for devices and diagnostics, and the benefit could be substantial.

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    References:

    1. Reynolds IS, Rising JP, Coukell AJ, et al. Assessing the safety and effectiveness of devices after US Food and Drug Administration approval: FDA-mandated postapproval studies. JAMA Intern Med. 2014;174(11):1773–1779.
    2. Unlocking Market Doors with Real-World Evidence [Internet]. Medical Product Outsourcing. 2019 [cited 25 May 2022]. Available from: https://www.mpo-mag.com/issues/2019-11-04/view_columns/unlocking-market-doors-with-real-world-evidence/
    3. deloitte.com. 2018 [cited 16 June 2022]. Available from: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/gx-lshc-medtech-iomt-brochure.pdf
    4. Pongiglione B, Torbica A, Blommestein H, de Groot S, Ciani O, Walker S et al. Do existing real-world data sources generate suitable evidence for the HTA of medical devices in Europe? Mapping and critical appraisal. International Journal of Technology Assessment in Health Care. 2021;37(1).
    5. Pandemic Accelerates Expanding Role Of Real-World Evidence In FDA Medical Device Submissions [Internet]. Meddeviceonline.com. 2020 [cited 28 May 2022]. Available from: https://www.meddeviceonline.com/doc/pandemic-accelerates-expanding-role-of-real-world-evidence-in-fda-medical-device-submissions-0001
    6. gov. 2017 [cited 25 May 2022]. Available from: https://www.fda.gov/files/medical%20devices/published/Use-of-Real-World-Evidence-to-Support-Regulatory-Decision-Making-for-Medical-Devices—Guidance-for-Industry-and-Food-and-Drug-Administration-Staff.pdf  
    7. Real World Evidence & EU MDR Compliance [Internet]. Mantra Systems Ltd. 2022 [cited 1 June 2022]. Available from: https://www.mantrasystems.co.uk/eu-mdr-compliance/real-world-evidence
    8. Optimizing Real-World Evidence in Medical Device Development – tHEORetically Speaking [Internet]. tHEORetically Speaking. 2020 [cited 1 June 2022]. Available from: https://blogsite.healtheconomics.com/2020/01/optimizing-real-world-evidence-in-medical-device-development/

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  • Tokenization in Real World Evidence Studies: the Concept and its Advantages

    Tokenization in Real World Evidence Studies: the Concept and its Advantages

    With all stakeholders increasingly realising the value real-world evidence (RWE) studies can bring into the healthcare delivery, newer applications of RWE are being discovered with each passing day. RWE has the potential to tremendously enhance the speed of patient access to new drugs. With this background, it is absolutely essential that the quality of RWE and the real-world data (RWD) that is used to generate RWE, are of high quality. To ensure quality RWD, it is also essential to bring about transparency into the RWD collection process. Unfortunately, this is easier said than done! However, various steps have been devised to improve transparency of RWD.[1] Two most prominent ones of these are pre-registration of RWE study protocol, and tokenization.

    The concept of data tokenization is not a new one, but the tokenization of health data is a fairly recent innovation. Tokenization of healthcare data is a process by which patient identifiers are de-identified through generation of a patient-specific ‘token’ that is encrypted.[2] It helps the researchers to link RWD from a patient’s previous medical history from diverse sources, and also aids tracking different active engagement across the healthcare system without any breach in the patient’s privacy.[2] Basically, all the sensitive data that can compromise a patient’s identity are replaced with unique identification symbols (tokens) that retain all the essential information, but without any compromises in the confidentiality. Tokenization can enable the access of health data without the need for decryption and re-encryption. The possibility of linking data with the use of tokens provides a more comprehensive understanding of health and health care.

    Since tokens are unique patient identifiers, they can help recognize a patient who appears across multiple sources of RWD. As the tokens created are patient-specific, the nature of token remains the same across different datasets. Since tokens do not contain any protected health information (PHI) of the patient, such as name, date of birth, and social security number, tokenization can protect against patient reidentification and loss of privacy/ confidentiality. Since the tokens are consistent across different formats of RWE, tokenization also helps to prevent duplication of data while collecting for RWE generation. In other words, tokenization acts as a ‘matchmaker’ to link patient data.[3] Since blockchain technology is generally used in creating tokens, the data is secure and free of manipulation.[4]

    Healthcare data tokenization has immense value from the viewpoint of all stakeholders in the healthcare industry. For clinical trial professionals, tokenization can improve study protocol design and can help in anticipating resources and support during a clinical trial. Tokenization also helps to expand follow-up data on trial participants and ensures representation of eligible participants. For healthcare providers, tokenization helps to match their patients’ clinical records accurately, enables keeping track of their patient’s clinical progress, and assists to combine healthcare data from different sources to allow new use cases.[5] For the pharmaceutical company and payers, tokenization provides immense value by helping analyse patient behaviour by tracking patient interactions with hospitals, clinics, pharmacies, laboratories, help groups, and other locations, in a completely de-identified manner. Thus, only the data relevant to the patient’s health is shared with the pharma, by keeping the patient’s personal details hidden. For the regulators, tokenization brings in an unmatched level of transparency in data, and the comprehensive data access that comes with healthcare data tokenization enables the regulators to perform appropriate and unbiased data review. Tokenization also helps data aggregators to meet regulatory compliances surrounding patient data confidentiality, such as HIPAA (Health Insurance Portability and Accountability Act) in the US. [6] Finally, patients can also benefit through tokenization of their health records. The confidentiality ensured by tokenization enables patients to securely organize, compile, share, and trade their personal medical records with relevant stakeholders.[7]

    The proliferation of tokenization software and other technologies helps in obtaining a more comprehensive approach of data. The USFDA advises using common data elements to provide a standard, consistent and universal data collection format for better results when linking of patient data is done. Maintaining the data integrity is a must as it may impact the overall scenario. The common issues can be redundant data, inconsistencies across data, privacy and data security. The integrity of data should be assessed for the compatibility and interoperability of data systems and the results produced must be consistent and repeatable, so that relevant data is obtained accurately.[8] Tokenization has the potential to improve the data integrity and enhance the quality of RWE studies.

    With the increasing stress by multiple stakeholders on data security and patient privacy, and increasing awareness about the importance of RWD transparency, and considering the unique advantages brought about by blockchain technology, healthcare tokenization has the potential to become a multibillion-dollar industry in the near future.

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    References

    1. Patorno E, Schneeweiss S, Wang SV. Transparency in real-world evidence (RWE) studies to build confidence for decision-making: reporting RWE research in diabetes. Diabetes Obes Metab. 2020 Apr;22 Suppl 3(Suppl 3):45-59.
    2. Weng I. Linking RWE to Clinical Trials. 2022. https://www.komodohealth.com/insights/linking-rwe-to-clinical-trials
    3. Dagenais S, Russo L, Madsen A, Webster J, Becnel L. Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design. Clin Pharmacol Ther. 2022;111(1):77-89.
    4. Scheuer E. Blockchain solves healthcare data obstacles. https://healthmanagement.org/c/hospital/issuearticle/blockchain-solves-healthcare-data-obstacles
    5. Healthcare Data Tokenization. https://risk.lexisnexis.com/healthcare/healthcare-tokenization
    6. Pezold A. HIPAA compliance requirements and tokenization. https://www.tokenex.com/blog/hipaa-compliance-and-tokenization .
    7. Dimitrov DV. Blockchain Applications for Healthcare Data Management. Healthc Inform Res. 2019 Jan;25(1):51-56.
    8. Trial Tokenization: Building A Bridge Between Clinical Trial Data And Real-World Data. E-Book. July 13, 2021.

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