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  • Role of AI in External Control Arm Development

    Role of AI in External Control Arm Development

    Role of AI in External Control Arm Development

    Real-world evidence (RWE) and real-world data (RWD) have been gaining prominence in the recent years, opening new avenues in clinical research, especially for settings where conventional randomized controlled trials (RCTs) are infeasible or ethically challenged. One such innovative step is the use of external control arms (ECAs), which consider retrospective patient data as comparators for single-arm clinical studies. This approach is particularly crucial in rare diseases, oncology, or therapeutic areas with high unmet need, where recruiting patients for a conventional control arm may be difficult or impossible. However, developing an effective and reliable ECA warrants careful matching of baseline characteristics, outcomes, and treatment pathways. These tasks are increasingly being supported and improved by the advent of artificial intelligence (AI).(1, 2)

    AI is instrumental in enhancing the development of ECAs by enabling quicker and more efficient extraction of relevant data from huge, heterogeneous RWD sources, such as electronic health records (EHRs), registries, claims databases, and even clinical notes. Through techniques like natural language processing (NLP) and machine learning (ML) algorithms, AI tools can accurately homogenize different data points, attribute missing values, and recognize eligible patient populations. This facilitates the selected external cohort to closely resemble the treatment population in terms of demographics, disease features, and prognostic factors, minimizing bias and increasing the reliability of the comparison.(1-4)

    Benefits of AI extend beyond just data selection by further facilitating vigorous statistical approaches in ECA development. Advanced ML approaches, such as propensity score matching, inverse probability treatment weighting, or even more adaptable models like generative adversarial networks (GANs), can be applied to calculate baseline covariates and replicate counterfactual outcomes.(5) These methods enable researchers to more precisely estimate treatment effects, improve causal inference, and minimize confounding, all of which are crucial elements to make ECA findings reliable to regulators, clinicians, and payers. Moreover, AI can help examine the validity of the ECA over time with evolving clinical practices, providing dynamic updates and adaptive practices.(2, 3, 5)

    However, the incorporation of AI in ECA development has several limitations. Data quality and completeness continue to be major challenges, as even the most innovative algorithms cannot counteract analytically missing or biased data. The interpretability and transparency of AI models are crucial for ensuring regulatory acceptance and duplicability.(2, 4) To address these concerns, stakeholders are promoting the use of explainable AI (XAI) and standardized validation frameworks to improve the understanding of model outputs, making them reliable to both scientific and non-scientific audiences.(6)

    As research progresses and explores the importance of ECAs in drug development, the combination of AI with RWD analytics is expected to transform evidence generation. By automating multifaceted analyses and discovering hidden patterns in large-scale datasets, AI is revolutionizing ECAs from a promising concept into a scalable, robust, and proficient solution that supports conventional clinical trial approaches. With the appropriate measures and collaboration across disciplines, AI-driven ECAs are immensely capable of expediting treatment access while maintaining scientific integrity and patient-centricity.

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    References

    1. Elvatun S, Knoors D, Brant S, et al. Synthetic data as external control arms in scarce single-arm clinical trials. PLOS Digit Health. 2025 Jan 23;4(1):e0000581.
    2. Pasculli G, Virgolin M, Myles P, et al. Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues. CPT Pharmacometrics Syst Pharmacol. 2025 May;14(5):840-852.
    3. American Statistical Association: AmstatNews: External Control Arms: Key Elements. June 2022. [Accessed online on 30th July 2025]. Available at: https://magazine.amstat.org/blog/2022/06/01/external-control-arms-key-elements/
    4. Singh, Ajit, Medical Data Imputation: Using Generative AI to Impute Missing Values in Medical Datasets (March 04, 2025). Available at SSRN: https://ssrn.com/abstract=5196881
    5. Kwak D, Liang Y, Shi X, et al. Comparing Machine Learning and Advanced Methods with Traditional Methods to Generate Weights in Inverse Probability of Treatment Weighting: The INFORM Study. Pragmat Obs Res. 2024 Oct 4;15:173-183.

    S Ali, T Abuhmed, S El-Sappagh, et al. Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence, Information Fusion. 2023; 99:101805.

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  • From Hashtags to Health Outcomes: Using Social Media Listening to Complement Real-World Evidence

    From Hashtags to Health Outcomes: Using Social Media Listening to Complement Real-World Evidence

    From Hashtags to Health Outcomes: Using Social Media Listening to Complement Real-World Evidence

    Real-world evidence (RWE) provides insights about the treatment efficacy in real-world setting and is crucial for patient-focused drug development. Traditionally, RWE is generated using real-world data (RWD) sourced from patient registries, electronic health records, or claims data. In addition, social media listening (SML) is an emerging frontier in RWE offering spontaneous, unfiltered patient expressions through platforms such as X (formerly Twitter), Facebook, Reddit, YouTube, and several health-related and disease-related forums (such as WebMD, AskAPatient, and PatientsLikeMe, etc). If harnessed responsibly, SML can complement traditional sources and provide insights otherwise overlooked.(1)

    The U.S. Food and Drug Administration (FDA) has acknowledged SML as an important avenue for generating patient experience data and incorporating insights into drug development. Similarly,  other global regulators including European Medicines Agency (EMA) and Medicines and Healthcare products Regulatory Agency (MHRA) have also supported the integration of patient experiences in clinical research, although specific SML guidelines are not yet available. Unlike surveys or interviews, SML captures patient narratives in their own words in real-time without influencing responses or burdens on the participants, offering authentic insights into emotional state, unmet needs and symptom burden.(1,2) Patient experiences captured in SML are often underrepresented in clinical research, especially in case of rare diseases or mental illness. Moreover, by collecting vast volume of narratives, SML enables monitoring of health trends.(1-3) Pharmacovigilance (PV) has been a key application of SML, since patients frequently post about adverse drug reactions and other drug experiences on different social media platforms.

    SML often involves three steps: identifying relevant social media data sources (including blogs, forums, platforms such as X, Facebook, etc); selecting content based on inclusion/exclusion criteria, and; coding patient experience such as social context, symptom impact, or treatment narratives. These methodological steps can be handled manually or algorithmically. Of late, artificial intelligence (AI) protocols involving machine learning (ML) and natural language processing (NLP) are improving scalability and precision of SML efforts.(1) The usage of AI has been perceived to improve the accuracy in identifying drug-event relationships while taking care of slangs, informal phrases or misspellings.(4,5). These tools can improve post-marketing surveillance in case of underreporting with traditional methods.

    Despite its potential, SML faces challenges such as skewed trends due to higher number of younger and tech-savvy users on social media, inducing selection bias. Demographic information of patients is not widely and accurately available on social media sources. Relevant social media posts might be fewer, and may contain false-positives. Additionally, data privacy and ethical concerns persist especially regarding commercial use of patient data. Most ethical frameworks insist SML data to be anonymized, publicly available, and used for public benefit.(1,2,4)

    To fully realize the potential of SML, stakeholders must collaborate with regulators and patient advocacy groups to establish clear guidelines and standardize analytical frameworks. Additionally, SML can be integrated into patient-reported outcomes to improve the quality of insights.(1) Thus, SML presents a powerful yet underutilized component of RWE, serving as a valuable component to improve patient-centricity, pharmacovigilance and bridging gaps in drug development.(3,5)

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    References:

    1. Cimiano P, Collins B, De Vuono MC, Escudier T, Gottowik J, Hartung M, Leddin M, Neupane B, Rodriguez-Esteban R, Schmidt AL, Starke-Knäusel C, Voorhaar M, Wieckowski K. Patient listening on social media for patient-focused drug development: a synthesis of considerations from patients, industry and regulators. Front Med (Lausanne). 2024 Mar 6;11:1274688.
    2. Cook NS, Kostikas K, Gruenberger JB, Shah B, Pathak P, Kaur VP, Mudumby A, Sharma R, Gutzwiller FS. Patients’ perspectives on COPD: findings from a social media listening study. ERJ Open Res. 2019 Feb 11;5(1):00128-2018.
    3. Wessel D, Pogrebnyakov N. Using Social Media as a Source of Real-World Data for Pharmaceutical Drug Development and Regulatory Decision Making. Drug Saf. 2024 May;47(5):495-511.
    4. Lavertu A, Vora B, Giacomini KM, Altman R, Rensi S. A New Era in Pharmacovigilance: Toward Real-World Data and Digital Monitoring. Clin Pharmacol Ther. 2021 May;109(5):1197-1202
    5. Sarker A, Ginn R, Nikfarjam A, O’Connor K, Smith K, Jayaraman S, Upadhaya T, Gonzalez G. Utilizing social media data for pharmacovigilance: A review. J Biomed Inform. 2015 Apr;54:202-12.

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  • Measuring the Invisible: Quantifying Bias in Real-World Evidence

    Measuring the Invisible: Quantifying Bias in Real-World Evidence

    Economic Impact of Climate Change on Health Systems

    In real-world evidence (RWE) studies, bias is a constant phenomenon, often driven by methods of patient selection, data capture, and defining outcomes.(1) Unlike randomized controlled trials (RCTs) that are designed to minimize systematic error, RWE studies often compete with the routine clinical practice and administrative data. Therefore, more than identifying bias; quantifying the distorted results is crucial.(1-4)

    Quantifying bias is different from simply adjusting for known issues. Quantification enables the estimation of the possible impact of systematic error on effect estimates, providing transparency and a guideline to evaluate the relaibility of findings. Several methods have been developed to support this.(5, 6)

    Quantitative Bias Analysis (QBA) is one of the most organized methodologies. It unambiguously mimics the direction and magnitude of bias through assumptions about misclassification, selection, or measurement error. Deterministic QBA offers corrected estimates with fixed scenarios, while probabilistic QBA helps describe the uncertainty in bias parameters through simulations. QBA essentially gives decision-makers a range of possible outcomes reflecting real-world imperfections, rather than giving a single ‘true’ estimate.(5-7)

    Another distinct tool is the E-value, which measures the minimum strength for an unmeasured bias, supported with both the exposure and outcome, to fully justify an observed correlation.(4, 5) A higher E-value represents more robust results against an unmeasured bias. E-values, although more commonly used for unmeasured confounding, can be a general standard for result stability across multiple types of bias.(5, 7, 8)

    Sensitivity analyses are supplementary to these tools as they assess the variability of results under different assumptions.(9) Whether it’s reconsidering exposure time windows, adjusting definitions of outcomes, or simulating different missing data scenarios, sensitivity analyses help assess the validity of a study’s conclusions under reasonable alternative conditions.(5, 7, 9)

    Negative control analyses also serve as an important tool to identify hidden bias. These work by causing unrelated exposures or outcomes for researchers to notice residual bias, which can often be missed by standard models. Spotting a signal where none should exist causes concerns for data validity or procedural flaws.(5-7)

    All these quantifying techniques don’t aim to eliminate bias, but rather to make its impact noticeable. For instance, all these parameters might show different levels of biases; yet, together, they move the interpretation from binary “yes/no” inferences to informed judgments about the amount of confidence in a particular evidence.(5-7)

    As RWE increasingly becomes an important driver of regulatory and clinical decisions, quantifying bias should become a routine practice. It facilitates transparency, duplicability, as well as more detailed conversations about evidence quality.

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    References

    1. Gokhale M, Stürmer T, Buse JB. Real-world evidence: the devil is in the detail. Diabetologia. 2020; 63:1694-1705.
    2. Kim HS, Kim JH. Proceed with Caution When Using Real World Data and Real World Evidence. J Korean Med Sci. 2019 Jan 16;34(4):e28.
    3. Maihöfner C, Mallick-Searle T, Vollert J, Kalita P, Sood Sethi V. Review of Challenges in Performing Real-World Evidence Studies for Nonprescription Products. Pragmat Obs Res. 2025; 16:7-18.
    4. Bykov K, Patorno E, D’Andrea E, et al. Prevalence of Avoidable and Bias-Inflicting Methodological Pitfalls in Real-World Studies of Medication Safety and Effectiveness. Clin Pharmacol Ther. 2022; 111(1):209-217.
    5. Petersen JM, Ranker LR, Barnard-Mayers R, MacLehose RF, Fox MP. A systematic review of quantitative bias analysis applied to epidemiological research. Int J Epidemiol. 2021 Nov 10;50(5):1708-1730.
    6. Shi X, Liu Z, Zhang M, Hua W, Li J, Lee JY, Dharmarajan S, Nyhan K, Naimi A, Lash TL, Jeffery MM, Ross JS, Liew Z, Wallach JD. Quantitative bias analysis methods for summary-level epidemiologic data in the peer-reviewed literature: a systematic review. J Clin Epidemiol. 2024 Nov;175:111507.
    7. Ramagopalan S, et al. Quantifying Bias in Real-World Studies: A New Hope for RWD Acceptance or Are HTAers Gonna Hate? [Accessed online on 4th July 2025]. Available at: https://www.ispor.org/docs/default-source/euro2022/ispor-ad-symposium-combined-slides—final-v4.pdf?sfvrsn=9e41ba3_0
    8. Barberio J, Ahern TP, MacLehose RF, et al. Assessing Techniques for Quantifying the Impact of Bias Due to an Unmeasured Confounder: An Applied Example. Clin Epidemiol. 2021; 13:627-635.
    9. Greenland, S. (2014). Sensitivity Analysis and Bias Analysis. In: Ahrens, W., Pigeot, I. (eds) Handbook of Epidemiology. Springer, New York, NY. https://doi.org/10.1007/978-0-387-09834-0_60

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  • Leveraging Registry Data to Support HTA and Payer Decision-Making

    Leveraging Registry Data to Support HTA and Payer Decision-Making

    Registry

    Health systems worldwide are increasingly realizing the need for well-timed, relevant, and patient-centred evidence to supplement the adoption and reimbursement decisions of new health technologies. Conventional clinical trial data, while crucial for validating safety and efficacy, often fail to represent real-world populations, long-term outcomes, or challenges in the routine clinical practice. Patient registries, which systematically collect patient data on a particular disease or treatment, can be useful in this context. Registry data are being increasingly emphasized as their application expands beyond the traditional role in post-marketing safety surveillance to the foundation for depicting real-world value and efficacy of health technologies.(1)

    Registries offer an exclusive, longitudinal view into patient outcomes, treatments, and healthcare resource utilization; making them a remarkably valuable source of real-world data (RWD) in the shift toward value-based care, where decisions made by healthcare systems must focus on the performance of health interventions in not just the randomized controlled trial (RCT) settings but across diverse and changing real-world populations.(2-4) For health technology assessment (HTA) agencies, registry data offer the ability to assess the efficacy of interventions in routine clinical practice, enable long-term outcome monitoring, and develop more rigorous economic models that account for real-life variables and healthcare resource utilization.(2, 5, 6)

    European and several other HTA bodies are increasingly adopting registry data to make decisions on disease burden, treatment patterns, cost-effectiveness, and comparative effectiveness, especially in areas where conventional RCTs are limited, such as rare diseases or personalized medicine. Registries also facilitate integration of vital insights into long-term safety profiles and the advancing patient experiences over time, elements that are usually beyond the scope of pre-market studies. For payers, this facilitates more certain and customized decisions about coverage, pricing, and resource allocation, particularly when connected with cost and budget impact models.(2, 5, 6)

    However, even with the growing significance of registry data, several structural and technical limitations restrict their widespread adoption in HTA and payer guidance. Data quality, standardization, and interoperability continue to pose challenges, while many registries still depend on internally specified criteria with few universally accepted standards, making cross-registry comparison difficult, which can even hamper the reliability of findings. Also, registries often struggle to keep up with the rapidly progressing health technologies, leaving evidence gaps when new interventions are introduced. These challenges highlight the growing need for standardized quality guidelines, transparent governance, and active data infrastructures that can adapt to the emerging evidence needs.(2, 5-8)

    Having said that, recent digital health advancements, decentralized study models, and integration with electronic health records (EHRs) and claims data are transforming the collection and use of registry data. These innovations lessen the patient and clinician burden, enhance data granularity and relevance, and improve interoperability across systems. They also facilitate connection with refined datasets to address a variety of regulatory, clinical, and economic queries, making registry-based real-world evidence (RWE) more robust, receptive, and relevant to decisions.(9, 10)

    With changes in regulatory landscapes, such as the introduction of European HTA Regulation (11) or the U.S. Inflation Reduction Act,(12) manufacturers, HTA agencies, and payers must rethink evidence types required for health technology submissions. A growing perception suggests that early and sustained collaboration between manufacturers, registries, regulators, and payers is crucial for aligning evidence generation approaches with decision-making requirements. This collective approach ensures the relevance of registries for future, while also supporting smarter, more unbiased, and impactful healthcare decisions.(2, 5, 6)

    Strategically expanding the use of registry data can help the healthcare ecosystem progress towards a patient-centred model, where access, affordability, and outcomes are driven by RWE. Recognizing this potential will require collected effort and careful investment, but it can provide significant value to patients, providers, and payers alike.

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    References

    1. Gliklich RE, Dreyer NA, Leavy MB, editors. Registries for Evaluating Patient Outcomes: A User’s Guide [Internet]. 3rd edition. Rockville (MD): Agency for Healthcare Research and Quality (US); 2014 Apr. 1, Patient Registries. Available at: https://www.ncbi.nlm.nih.gov/books/NBK208643/
    2. Murphy LA, Akehurst R, Cunningham D, et al. Real-world evidence to support health technology assessment and payer decision making: is it now or never? Int J Technol Assess Health Care. 2025 Mar 31;41(1):e20.
    3. MDIC HEPV Initiative. Payer and HTA Perspectives on Real-World Evidence for Medical Devices – Final Report. November 2021.
    4. Jaksa A, Arena PJ, Hanisch M, Marsico M. Use of Real-World Evidence in Health Technology Reassessments Across 6 Health Technology Assessment Agencies. Value Health. 2025; 28(6):898-906.
    5. Windfuhr F, de Vries ST, Melinder M, et al. Stakeholders’ Perspectives Toward the Use of Patient Registry Data for Decision-Making on Medicines: A Cross-Sectional Survey. Drug Saf. 2025 Feb 23.
    6. Evidera. Advancing the Use of Registry Data to Improve Health Technology Assessment and Payer Evidence. ISPOR 2024. Available at: https://www.ispor.org/docs/default-source/intl2024/120eviderav2.pdf?sfvrsn=a1d01ffe_0
    7. Rubinger L, Ekhtiari S, Gazendam A, Bhandari M. Registries: Big data, bigger problems? Injury. 2023; 54(3):S39-S42.
    8. Allen A, Patrick H, Ruof J, et al. Development and Pilot Test of the Registry Evaluation and Quality Standards Tool: An Information Technology–Based Tool to Support and Review Registries. Value Health. 2022; 25(8):1390-1398.
    9. Christian J, Dasgupta N, Jordan M, et al. Digital Health and Patient Registries: Today, Tomorrow, and the Future. In: Gliklich RE, Dreyer NA, Leavy MB, et al., editors. 21st Century Patient Registries: Registries for Evaluating Patient Outcomes: A User’s Guide: 3rd Edition, Addendum [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2018 Mar. 3. Available at: https://www.ncbi.nlm.nih.gov/books/NBK493822/
    10. Miller RS, Mitchell K, Myslinski R, et al. Health Information Technology (IT) and Patient Registries. 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 1. Available at: https://www.ncbi.nlm.nih.gov/books/NBK551883/
    11. European Commission. Implementation of the Regulation on health technology assessment: Regulation (EU) 2021/2282 of The European Parliament and of The Council of 15 December 2021 on Health Technology Assessment and Amending Directive 2011/24/EU. Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32021R2282
    12. IRS. Inflation Reduction Act of 2022. Available at: https://www.irs.gov/inflation-reduction-act-of-2022

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  • The Role and Importance of SLRs and RWE in Drug Price Negotiations in the USA

    The Role and Importance of SLRs and RWE in Drug Price Negotiations in the USA

    The cost of prescription drugs is a significant burden on patients and the healthcare system, especially in countries such as the USA. High drug prices can strain government programs, such as Medicare and Medicaid, and private insurers, which can lead to higher premiums for consumers. Additionally, high drug prices are responsible for increased out-of-pocket expenses for patients, which further lead to medication non-adherence, and thus result in poorer health outcomes. On the other hand, the research and development activities in pharmaceutical industries depend on their profit from sales, and an extremely harsh reduction in drug prices can have adverse consequences in terms of a lack of incentive for innovation in the pharmaceutical industry. (1)

    All these factors make responsible and reasonable price negotiation an extremely important process in the market access cycle for drugs. By negotiating drug prices appropriately, the government and payers can help ensure that patients have access to affordable medications while also promoting competition and innovation in the industry. (1)

    Price negotiations for drugs in the US are typically done by insurers and government programs, such as Medicare and Medicaid. The negotiation process is complex, involves multiple factors, and largely depends on the payer and the drug; the typical steps and factors considered include formulary placement, rebates and discounts, value-based arrangements, price controls, and competitive bidding. (2) To this effect, evidence on clinical effectiveness becomes extremely important, and it is essential that there is a robust and ethical body of evidence to display that the new innovation is efficacious, safe, and brings about enough value to justify the premium that the patients and payers are asked to pay for accessing the intervention.

    The traditional sources for clinical effectiveness evidence for the purpose of price negotiation of drugs are the same as those for marketing approval, and are largely constituted by Randomized Clinical Trials (RCTs), which are often conducted by pharmaceutical companies to demonstrate the safety and efficacy of their drugs. These trials are designed to meet regulatory requirements and are often submitted to the FDA as part of the drug approval process. (3)

    However, it is being increasingly realized that evidence in addition to traditional RCTs can play a crucial role in determining the true extent of efficacy, safety, and value of an intervention in a particular therapy area or patient population. Specifically, increasing interest is being shown towards using evidence from systematic literature reviews (SLRs) and real-world evidence (RWE) for informing clinical effectiveness data for drug price negotiations. (3, 4)

    SLRs, being comprehensive evaluations of existing research studies, provide a balanced summary of the available evidence on a drug’s safety, efficacy, and cost-effectiveness, and thus can be an invaluable resource for drug price negotiations. However, using SLRs is associated with some challenges pertaining to the varying quality of evidence of studies included in the SLR, publication bias (by which there is an overestimation of a drug’s effectiveness due to non-publication of many studies with negative results), time and resource constraints, conflicts of interest, and lack of generalizability. (4)

    RWE coming from the analysis of different sources such as electronic health records, claims data, and patient registries, can provide insights into how drugs are used in actual clinical practice, including their safety and effectiveness over time. By demonstrating the value of a drug in real-world settings, RWE can provide details about the actual usage pattern of an intervention post its marketing, compared to RCTs which offer a view of clinical effectiveness from a restricted population, prior to marketing. However, using RWE is also associated with certain challenges, such as quality of data, reliability, lack of data standardization, data interoperability, privacy concerns, and concerns about the quality of data analysis leading to generation of RWE. (3)

    Health authorities worldwide have taken several initiatives to include Systematic Literature Review and RWE as key elements in market authorization and in price and reimbursement negotiations. Of more credit, is the fact that in the USA, the 21st Century Cures Act specified that RWD could be used to support the approval of a new indication for a drug that is already approved or to support or satisfy post-approval study requirements. (5-7)

    Interestingly, the Inflation Reduction Act (IRA) is the biggest landmark set by United States federal law to curb inflation by reducing the deficit and lowering prescription drug prices. (5) This Act established a drug price negotiation program within the department of Health and Human Services (HHS), enabling the Secretary to negotiate the prices of certain costly drugs within the Medicare program. The Centres for Medicare & Medicaid Services (CMS), through the U.S. Department of Health and Human Services (HHS), released initial guidance outlining the conditions and limitations of the new Medicare Drug Price Negotiation Program for 2026. The Medicare Drug Pricing Negotiation Program and other provisions in the new drug law will improve Medicare’s capacity to serve those enrolled in the program and future generations of Medicare recipients. (5-8)

    SLRs and RWE have an important role to play in generating clinical evidence for drug price negotiations, and the USFDA is in the process of regulating the steps needed for this by drafting the guidance document.  Together with other provisions in the new drug law, the Medicare Drug Price Negotiation Program will increase Medicare’s ability to serve current Medicare beneficiaries as well as future generations. (8)

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    References

    1. Papanicolas I, Woskie LR, Jha AK. Health care spending in the United States and other high-income countries. Jama. 2018 Mar 13;319(10):1024-39.
    2. Gruber J. Delivering public health insurance through private plan choice in the United States. Journal of Economic Perspectives. 2017 Nov 1;31(4):3-22.
    3. Pulini AA, Caetano GM, Clautiaux H, et al. Impact of Real-World Data on Market Authorization, Reimbursement Decision & Price Negotiation. Ther Innov Regul Sci. 2021 Jan;55(1):228-238.
    4. Tarsilla M. Cochrane handbook for systematic reviews of interventions. Journal of Multidisciplinary Evaluation. 2010;6(14):142-8.
    5. Levitt. The Inflation Reduction Act Is a Foot in the Door for Containing Health Care Costs, JAMA Health Forum. 3 (2022) e223575.
    6. A Turning Point for U.S. Climate Progress: Assessing the Climate and Clean Energy Provisions in the Inflation Reduction Act | Policy Commons. https://policycommons.net/artifacts/2649285/a-turning-point-for-us-climate-progress_inflation-reduction-act/3672158/
    7. Inflation Reduction Act Guidebook | Clean Energy | The White House. https://www.whitehouse.gov/cleanenergy/inflation-reduction-act-guidebook
    8. Sullivan SD. Medicare Drug Price Negotiation in the United States: Implications and Unanswered Questions. Value Health. 2023 Mar;26(3):394-399.

     

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  • Unlocking the Power of RWE: Changing Scenario in the Global Landscape

    Unlocking the Power of RWE: Changing Scenario in the Global Landscape

    There has been a shift in the global healthcare ecosystem from volume-based to value-based payment model, thanks to a surge in data availability, interoperability, advancing health technologies, cost and competitive pressures, scientific advances, and increasing adoption of personalized medicine. The resulting availability of a large quantity of real-world data (RWD) has made it possible to perform continual observation of disease epidemiology, treatment patterns, and outcomes in the real world. Analysing strong RWD generates strong real-world evidence (RWE), and the incredible power of RWE in the drug approval process, including prioritizing and streamlining drug development, is being realised by all stakeholders. RWE especially gains importance because randomized controlled trials (RCTs) cannot be applied to the entire patient population of a specific disease. Parallel to this, the value, usage, and acceptance of RWE in the pharmaceutical and biotechnology industries have also increased in recent years.[1]

    RWE is increasingly used by the regulators in the drug and device approval cycle, for safety evaluation, updating label claims, and for new usage approvals, as a supplement to RCTs for improved understanding of efficacy and safety of medical products and devices. However, the main concern in the usage of RWE lies in the robustness and quality of RWD. Since RWD is mined under uncontrolled settings from data often collected without a pre-defined objective, there is a possibility of data inconsistencies and spurious results, leading to a relatively lower quality of data compared to RCT data. Data quality has been defined to ensure conformance, completeness, and plausibility,[2] and to achieve high quality of RWD, there is a need for uniform regulatory guidelines and frameworks surrounding RWD collection and analysis.

    Globally, regulatory bodies are showing interest in adopting RWE as a component of the decision-making process to complement RCT evidence by strengthening the guidelines and framework for including RWD. For example, in the USA, the 21st Century Cures Act and Prescription Drug User Fee Act recommend the use of RWE, as a supplement to RCTs evidence, for regulatory decision-making and approval of drugs. In December 2018, the USFDA released a framework for the USFDA’s RWE program for evaluating the potential use of RWE for approval support to drugs and biologics.[3] The key considerations in the USFDA RWE program are: RWE must be ‘fit for use’; trial/study designs should provide adequate scientific evidence; and RWE must comply with the USFDA regulatory requirements. [4] In addition to the USFDA’s efforts, several other initiatives, such as the Clinical Trial Transformation initiative, Friends of Cancer research, are working to optimize RWD, developing new study methods, and refining RWD analytics.[3,5]

    In the UK, an RWD framework has been structured to ensure that the collected RWD is of relevance, provenance, and sufficient quality. The framework ensures relevancy, transparency at all levels of study planning, conduct, and reporting, and robust analytics to minimize bias and uncertainty. This living framework is being periodically updated based on user feedback and practice.[6] In Europe, the EMA launched the OPTIMAL (OPerational, TechnIcal, and MethodologicAL) framework in 2019 to explore the pertinent use of valid RWE for regulatory purposes.[7]

    Similarly, Health Canada is working with the Canadian Agency for Drugs and Technologies in Health (CADTH) and the National Institute of Excellence in Health and Social Services (INESS) to establish a joint document to optimize the use of RWE. In Japan, the Pharmaceuticals and Medical Devices Agency (PMDA) established regulatory guidelines in March 2021 for the use of registries, to ensure the reliability of RWD and RWE.[4]

    In addition to these efforts, the ICH-GCP has also established plans to harmonize global RWE and update its existing E6 (General considerations in clinical trials) and E8 (Guidelines for Good Clinical Practice) guidelines. The CIOMS (Council of International Organisations of Medical Sciences) is currently developing a consensus report and recommendations for the use of RWE in the regulatory decision-making process.[4]

    The shift from restricted uses of traditional evidence sources (RCT) to wider adaptation of newer modes (RCT + RWE) in different regions of the world is a positive sign showing a global increase in the value of RWE in research, practice, and policymaking. RWE undoubtedly serves as a key to a more robust, less expensive, and more inclusive approach to better healthcare through research. Undoubtedly there are gaps in the use of RWE at present: some countries have more acceptance and activity than others. There is a need for development of realistic and robust standards and best practices to ensure the quality of RWD used in RWE. Recommendations and uniform guidelines are needed across the world to shape, harmonize and generate reliable RWE. Nonetheless, the future holds a good promise on RWE.

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    References:

    1. Khosla S et al. Real world evidence (RWE) – a disruptive innovation or the quiet evolution of medical evidence generation? F1000Research. 2018;7:111.
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