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  • Advancing Health Technology Assessment Through R Adoption and Standardization

    Advancing Health Technology Assessment Through R Adoption and Standardization

    The increasing implementation of the R programming language in health technology assessments (HTAs) is the result of the need for transparency, reproducibility, and efficiency in health economic modelling. Unlike conventional excel-based tools, R provides a completely script-based setting that records every step of the modelling process, from data import to simulation and reporting. This improves auditability and error reduction while also enabling smooth automation, making R perfectly suitable to cater to the rising demand for “living HTAs” that evolve with new evidence.(1, 2)

    HTA bodies, including NICE (the UK) and ZIN (the Netherlands) are adopting R-based submissions, indicating rising institutional confidence in open-source, code-driven methodologies.(3) The ability of R programming manage multifaceted systems, incorporate version control, and automate analyses is changing how HTAs are performed. Academic and industry partnerships are creating shared frameworks and toolkits to further simplify these processes, facilitating consistent, transparent, and faster decision-making.(1, 3)

    Standardisation is the most crucial factor of R adoption. For this, validated and reusable modelling frameworks are being developed to help regulators.(4) Initiatives like the open-source assertHE package integrate validation and quality checks right into modelling workflows, supporting built-in verification rather than retrospective review.(5) These frameworks reduce review time, enhance reproducibility, and facilitate efficient model adaptation across markets, thus striking a balance between innovation and rigour. The growing number of health economists equipped with R expertise further reinforces this ecosystem, shifting toward code-based submissions that are easier to review, update, and share.(1, 4, 5)

    The move toward standardisation also facilitates scalability in global HTAs. R also facilitates country-specific modifications through modular inputs rather than structural model changes, maintaining consistency across jurisdictions. Shared code sources, scenario templates, and uniform data structures are helpful in cross-country comparisons, making them more reliable and less resource-intensive.(1, 3, 4)

    R’s versatility goes beyond modelling efficiency to support real-world data (RWD) and artificial intelligence (AI) integration, which are the crucial pillars of HTA evidence bases. R’s capacity for secure data handling, API-based automation, and remote computation enables models to advance dynamically while maintaining data privacy. However, challenges, especially about data quality, interoperability, and achieving methodological agreement across agencies, persist. Overcoming these warrants collective participation from academia, regulators, and industry to establish shared standards and training guidance.(2, 3, 6)

    Finally, the implementation and standardisation of R signify a critical step towards making HTAs more transparent, reproducible, and globally aligned. By adopting open-source technology and collaborative validation, R is transforming the assessment of health technologies, taking HTA from static assessments into dynamic, data-driven systems that adapt to evidence and policy needs of the real-world.

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    References

    1. Smith RA, Schneider PP, Mohammed W. Living HTA: Automating Health Economic Evaluation with R. Wellcome Open Res. 2022; 11(7):194.
    2. R Consortium. R for Health Technology Assessment (HTA): Identifying Needs, Streamlining Processes, Building Bridges. Accessed online on 10th November 2025. Available at: https://r-consortium.org/posts/r-for-health-technology-assessment-hta-identifying-needs-streamlining-processes-building-bridges/
    3. Poerrier JE, Ettinger J, Bergemann R. R in HEOR modelling for HTA submissions: An assessment. Accessed online on 10th November 2025. Available at: https://www.parexel.com/application/files/2917/2729/8142/FY24_R_in_HEOR_Modelling_White_Paper_09-2024_v3.pdf
    4. Thokala, P., Srivastava, T., Smith, R. et al. Living Health Technology Assessment: Issues, Challenges and Opportunities. PharmacoEconomics. 2023; 41:227–237.
    5. Smith RA, Samyshkin Y, Mohammed W, et al. assertHE: an R package to improve quality assurance of HTA models. [version 1; peer review: 1 approved, 1 approved with reservations]. Wellcome Open Res. 2024; 9:701.
    6. Zisis K, Pavi E, Geitona M, Athanasakis K. Real-world data: a comprehensive literature review on the barriers, challenges, and opportunities associated with their inclusion in the health technology assessment process. J. Pharm. Pharm. Sci. 2024; 27:12302.

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  • Selecting Appropriate Review Approach for HTA Submissions

    Selecting Appropriate Review Approach for HTA Submissions

    Formulating Fit-for-Purpose Evidence Packages to Demonstrate the Value of Interventions for Rare Diseases

    Health Technology Assessments (HTAs) heavily rely on vigorous evidence synthesis. As a result, the selection of review methodology for evidence generation is an essential early step in the HTA process. This selection usually depends on the research question, nature and extent of existing evidence, and assessment timelines.(1) Three distinct approaches for reviewing existing evidence include de novo systematic review, an update of an existing review, and an overview of systematic reviews.(1, 2)

    A de novo systematic review is performed when no reliable review is available on the topic, or when the available ones are outdated, incomplete, or methodologically weak. This method enables an exhaustive and unbiased evaluation of the evidence from scratch, allowing for the customization of inclusion criteria, quality appraisal, and data synthesis as per the specific HTA’s objectives. This is also the most resource-consuming option, demanding substantial time and effort to select and analyse the relevant literature.(2-4)

    An update of an existing systematic review may be performed if a high-quality review is already present in public domain; here the objective would be to update the evidence using publications reported after the publication of the review. In rapidly evolving therapeutic areas with frequently emerging trials and interventions, updating an existing review provides efficiency while preserving the integrity of the evidence. This approach facilitates HTA bodies to expand on prior work, integrate new data, and improve inferences without repeating the entire review process.(2, 4, 5) In this context, the use of living systematic reviews (living SLRs), which are constantly updated with new and upcoming evidence, can also be explored. By integrating real-time updates, living SLRs minimize the delay between research publication and evidence generation, making them especially important in dynamic therapeutic areas where well-timed decisions are crucial.(6, 7)

    An overview of systematic reviews, often referred to as an umbrella review, is apt when multiple high-quality reviews have already been performed on closely related interventions or outcomes. This methodology incorporates findings across reviews, focusing on not just consistencies but also differences and evidence gaps. It is especially important when HTAs are required to assess wide-ranging policy questions or making decisions encompassing multiple treatment options, therapeutic areas, or patient subgroups. By incorporating insights across systematic reviews, their overviews offer an enhanced understanding of the evidence setting that guides strategic decision-making.(2, 3, 8)

    In some cases, selection of other types of reviews may also be applicable. For instance, scoping reviews can be especially beneficial during the exploratory phase, where the aim is to determine the scope of available evidence, elucidate concepts, or characterize knowledge gaps before providing final answers. Such scoping reviews help refine research questions and assess if a full systematic review is needed.(1) On the other hand, rapid reviews are used when HTA-relevant decisions must be made under strict timelines (e.g., during public health emergencies or early advice procedures), in which conventional systematic review steps are streamlined while preserving transparency. In contrast, targeted literature reviews are applied in narrower contexts, such as epidemiology assessments or comparator identification, where a structured but not fully systematic evidence synthesis is sufficient. However, both rapid and targeted reviews are generally less favoured in HTA because their methodological shortcuts increase the risk of bias and limit reproducibility compared with full systematic reviews.(9, 10) Agencies like Canadian Agency for Drugs and Technologies in Health (CADTH) are leading the development of practical guidance on when each review approach is most suitable, facilitating global HTA agencies to approve fit-for-purpose methods.(2)

    Finally, the selection of any of these review types should be based on the scope of the HTA, available timelines, and the state of the evidence landscape. A rationalized decision not only facilitates methodological strength but also improves the integrity and relevance of the HTA’s recommendations. With the expanding evidence network, careful selection of the review approaches will continue to be essential for accomplishing timely, efficient, and impactful HTAs.

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    References

    1. Nemzoff C, Shah HA,  Heupink LE, et al. Adaptive Health Technology Assessment: A Scoping Review of Methods. Value Health. 2023; 26(10):P1549-1557.
    2. Kim JSM, Pollock M, Kaunelis D, Weeks L. Guidance on review type selection for health technology assessments: key factors and considerations for deciding when to conduct a de novo systematic review, an update of a systematic review, or an overview of systematic reviews. Syst Rev. 2022; 11(1):206.
    3. Pollock M, Fernandes RM, Becker LA, et al. What guidance is available for researchers conducting overviews of reviews of healthcare interventions? A scoping review and qualitative metasummary. Syst Rev. 2016;5(1):190.
    4. Cumpston M, Chandler J, et al. Chapter IV: Updating a review. In: Higgins J, Thomas J, Chandler J, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions. Version 6.2 (updated February 2021) London: Cochrane; 2021.
    5. Garner P, Hopewell S, Chandler J, et al. When and how to update systematic reviews: consensus and checklist. BMJ. 2016; 354:i3507.
    6. Simmonds M, Elliott JH, Synnot A, Turner T. Living Systematic Reviews. Methods Mol Biol. 2022; 2345:121-134.
    7. Sauca M, Tarchand R, Kallmes K. Living SLRs for HTA. ISPOR Europe 2023. [Accessed online on 1st Sept 2025]. Available at: https://www.ispor.org/docs/default-source/euro2023/isporeurope23saucahta361poster-129656-pdf.pdf?sfvrsn=ca7a753b_0
    8. Pollock M, Fernandes RM, Becker LA, Pieper D, Hartling L. Chapter V: Overviews of reviews. In: Higgins JPT, Thomas J, Chandler J, et al., Cochrane Handbook for Systematic Reviews of Interventions. Version 6.2 (updated February 2021). London: Cochrane; 2021.
    9. Kaltenthaler E, Cooper K, Pandor A, et al. The use of rapid review methods in health technology assessments: 3 case studies. BMC Med Res Methodol. 2016; 16(1):108.
    10. Watt A, Cameron A, Sturm L, et al. Rapid reviews versus full systematic reviews: an inventory of current methods and practice in health technology assessment. Int J Technol Assess Health Care. 2008; 24(2):133-9.

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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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  • 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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  • Reporting Characteristics Of Systematic Reviews For The UK NIHR HTA programme

    Reporting Characteristics Of Systematic Reviews For The UK NIHR HTA programme

    Systematic reviews (SRs) are incredibly crucial for healthcare decision-making, as they often provide a reliable summary of evidence on the comparison among healthcare interventions. They identify, assess, and combine the results of similar but individual studies and help to clarify the known and unknown benefits and risks associated with drugs, devices, and other healthcare interventions. SRs are helpful for clinicians to incorporate research findings into their daily practices, for patients to make informed choices about their care, and for professional medical organizations to develop clinical practice recommendations. (1)

    Their importance in clinical decision-making has led to the rising number of published SRs. Consequently, the quality of published SRs is also being questioned. Findings of a cross-sectional study by Page et al. (2016) have observed poor quality of conduct and reporting of many SRs, despite the availability of the relevant reporting standards, such as the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. (2) This is a big issue since poorly conducted and reported SRs often give away misleading conclusions that significantly affect decision-making. (2,3)

    Cochrane SRs, published in the Cochrane Database of Systematic Reviews, are typically considered the ‘gold standard’ (4) and superior to other non-Cochrane SRs, are often contracted by policy-makers, and involve many safeguards against the potentially ‘misleading conclusions’.(3) Health Technology Assessment (HTA) is a specific area where an SR providing evidence on the clinical efficacy of a medical device/technology plays a crucial role in decision-making and would represent a different type of ‘non-Cochrane’ review. One such group of SRs is conducted for the UK National Institute of Health Research (NIHR) HTA programme (HTA-SRs). The full texts of HTA-SRs are published in the programme’s own journal, Health Technology Assessment (Winchester). (5)

    The HTA-SRs show a remarkable comparison with Cochrane reviews as they have clear reporting standards and are not limited by word restrictions or the lack of online appendices, unlike many other non-Cochrane reviews published in conventional peer-reviewed journals. The nature and reporting of HTA-SRs, Cochrane, and non-Cochrane reviews are similar in many aspects but different in several other areas, such as, a more extensive range of included studies, the failure of HTA-SRs to report the total number of participants from included and instead reporting a range, the availability and registration of SR protocols, and a statement specifying adherence to respective guidelines while conducting the review. Whereas, concerning the conduct and reporting of the study selection, data extraction, and critical appraisal processes, the HTA-SRs have been observed to be better reported than non-Cochrane reviews.(3)

    Predominantly, it has been observed that the reporting characteristics of HTA-SRs published in the Health Technology Assessment journal are analogous to Cochrane reviews and better than many other non-Cochrane reviews in several aspects. These include identification as an SR, review registration and protocol availability, conflicts of interest, mentioning the type of literature included, particulars of the strategies for database search, trial registry and grey literature search, the use of PRISMA flow diagrams, providing details of any excluded studies; and the reporting of limitations at the review level and of the included studies. All these factors impact informed decision-making, particularly the one that highlights the importance of sourcing unpublished data, (6,7) and explains uncertainties within the evidence-base and review itself.(3) Apparently, HTA-SRs have weaker reporting than Cochrane reviews across several characteristics, as reported earlier. HTA-SRs are also not expected to develop recommendations based on the quality of the evidence since that responsibility is on the other groups in the HTA process. (8)

    The conduct and reporting of Cochrane reviews and those published in the UK Health Technology Assessment journal are thus of the same standard and usually better than many other non-Cochrane reviews. Accordingly, the HTA-SRs should arguably be considered equivalent to supposed Cochrane ‘gold standard’, and not be clubbed together with other non-Cochrane reviews. They are approved by regulators from the UK Department of Health (National Institute of Health and Care Excellence [NICE] and the NIHR) with a particular policy-making and decision-making objective and audience in mind.(5)  Also, it would not be wrong to say that any systematic reviews conducted for the purpose of HTA should follow the reporting guidelines of the HTA-SRs. The processes for conducting and reporting SRs for HTA have to be transparent, rigorous, and of the highest quality.  Bias in the review needs to be reduced, and the possibility of ‘misleading conclusions’ cannot be allowed in HTA-SRs because there is a lot at stake.(3)

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    References

    1. Institute of Medicine. 2011. Finding What Works in Health Care: Standards for Systematic Reviews. Washington, DC: The National Academies Press.
    2. Page M, Shamseer L, Altman D, et al. Epidemiology and reporting characteristics of systematic reviews of biomedical research: a cross-sectional study. PLoS Med 2016; 13(5):e1002028.
    3. Carroll C, Kaltenthaler E. Nature and reporting characteristics of UK health technology assessment systematic reviews. BMC Medical Research Methodology 2018; 18:35.
    4. Cochrane library. Available at: https://www.cochranelibrary.com/about/about-cochrane-reviews
    5. National Institute for Health Research (NIHR): Health Technology Assessment Programme. Available at: https://www.nihr.ac.uk/funding-and-support/funding-forresearch-
    6. studies/funding-programmes/health-technology-assessment/.
    7. Jones C, Keil L, Weaver M, et al. Clinical trials registries are underutilized in the conduct of systematic reviews: a cross-sectional analysis. Syst Rev 2014; 3:126.
    8. Hart B, Lundh A, Bero L. Effect of reporting bias on meta-analyses of drug trials: reanalysis of meta-analyses. BMJ 2012; 344:d7202.
    9. Rotstein D, Laupacis A. Differences between systematic reviews and health technology assessments: a trade-off between the ideals of scientific rigor and the realities of policy making. Int J Technol Assess Health Care 2004; 20:177–83.

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  • What Are The Pros & Cons of Network Meta-Analysis (NMA)?

    What Are The Pros & Cons of Network Meta-Analysis (NMA)?

    Evidence-based medicine (EBM) is gaining wide acceptance from researchers globally as it thoroughly optimizes the latest available evidence to make informed care decisions. This involves evaluating the quality of the clinical data by critically assessing methodologies reported in publications. Moreover, EBM incorporates both clinical expertise as well as patient values. Meta-analyses of RCTs often make it among the top of the evidence hierarchy, since it’s regarded as the most valid clinical proof. Indeed, meta-analysis is a validated method to analyse and summarize knowledge by increasing the number of patients, and thus also the effective statistical power. However, there are several limitations associated with meta-analysis, which considers only pairwise comparisons. Unfortunately, head-to-head comparisons are not always available in the literature or they fail to answer a specific clinical question. This can be overcome with the help of network meta-analysis (NMA), which helps providing a global estimate of efficacy or safety of numerous experimental treatments that have not before been directly compared with adequate precision, or at all. Network meta-analysis integrates both direct and indirect effects from the entire set of evidence. Additionally, it ranks the treatments as the best or worst on the basis of valid statistical inference methods. (1)

    Network meta-analysis is preferable over conventional pair-wise meta-analysis, since it uses indirect evidence to justify comparisons amongst all treatments, thus enabling estimation of comparative effects that have not been investigated as precisely in RCTs. Thus, NMA is increasingly getting popular with clinicians, guideline developers, and HTA agencies as the ever growing new evidence needs to be placed in the context of all available evidence for appraisals. (2)

    Furthermore, NMA is increasingly becoming essential to formulating recommendations on reimbursements as well as clinical guidelines by healthcare agencies around the world. It has recently been adopted by Cochrane, as 10% (23/230) of their systematic reviews since the year 2015 have used NMA. In 2015, GRADE working groups published guidance on using GRADE in conjunction with NMA. Furthermore, the National Institute for Health and Clinical Excellence (NICE, UK) also approves the application of NMA within its clinical guidelines manual. (3)

    In addition, the ability of NMA to quantitatively assess interventions that have not been directly compared in studies aids the process of developing guidelines. This is because, in the absence of head-to-head evidence, guideline development groups will bank more strongly on expert opinion. Also, collective analysis of both direct and indirect evidence strengthens the evidence base. Moreover, while NMA determines the comparative effectiveness of drugs, the approach can be applied more broadly. For instance, the latest revision of the WHO HIV guidelines represents how the use of NMA to evaluate interventions in improving adherence to ART significantly contributed in the formulation of recommendations. (4)

    Although NMA is a powerful tool for comparative effectiveness research, it is more complex than pair-wise meta-analysis. Also, the assumption of transitivity is stringent, which is essential to consider throughout the entire process of NMA. Supplementary analyses like network meta-regression are often necessary, which further increase the complexity of the analysis. What’s more, NMA is very resource-demanding. As NMAs generally cater to broader questions, they usually involve more studies at each step of the systematic review, right from screening to analysis, than conventional meta-analysis. Therefore, it is crucial to schedule the time and resource commitment before actually conducting an NMA. (5)

    Network meta-analysis allows for indirect comparisons, incorporating more data in the analysis, thus tackling the bigger picture; while a single pairwise meta-analysis offers a fragmented picture. However, NMA should be done carefully. There needs to be a clinical question developed with input from both a subject area clinical expert and a statistician. Assessments of transitivity and consistency are fundamental for ensuring the validity of NMA. Finally, as mentioned earlier, the time and resource commitments required to produce a high-quality NMA should be appropriately taken into consideration. (5)

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    References

    1. Greco T, et al. The attractiveness of network meta-analysis: a comprehensive systematic and narrative review. Heart, Lung and Vessels 2015; 7(2):133-142.
    2. Ward P. Network Meta-Analysis Can Play Decisive Role. June, 2013.
    3. Kanters S, et al. Use of network meta-analysis in clinical guidelines. Bulletin of the World Health Organization 2016; 94:782-784. 
    4. Consolidated guidelines on HIV prevention, diagnosis, treatment and care for key populations. 2016 update. Geneva: World Health Organization; 2016. 
    5. Rouse B, Chaimani A, Li T. Network meta-analysis: an introduction for clinicians. Intern Emerg Med 2016; 12(1):103-111.

    Written by – Ms. Tanvi Laghate

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  • How Early HTA Methods are Helping Healthcare Companies in Decision Making?

    How Early HTA Methods are Helping Healthcare Companies in Decision Making?

    Heath Technology Assessment (HTA) is a scientific research area that makes informed clinical as well as policy decisions on the use of health technologies, which include pharmaceuticals, medical devices, diagnostics, procedures and other clinical, public health and organizational interventions. (1)

    Recently, ‘early HTA’ is progressively being promoted as a perspective to determine added value of potential new technologies early in the development channel. Such kind of assessment would be useful to (1) make decision for further development of the technology, (2) to fix minimum performance thresholds for the new technology compared to currently available technologies and, (3) to sustain pricing and reimbursement in early stages of development. It thus considers existing regulatory requirements as well as mechanisms for obtaining reimbursement depending on the added value produced. Early HTA differs from mainstream HTA with respect to informed decisions made by R&D instead of government (agencies) about coverage. (2)

    Health economic assessments during early stage informs decisions on the commercial viability of new medical technologies, thus allowing companies to discontinue further development if results suggest possible failure of the product. Evidence shows increasing number of researchers stressing on early HTA of medical devices. While most of the former work is about informing decisions in drug development, there has been a rapid increase in studies evaluating medical devices since last decade. (3,4) This is owing to the global trend building strong regional medical technology innovation clusters with support of (local) governments. Success of a medical technology hugely depends on the value created in the health system in terms of patient outcomes, convenience, or sustainability of care. Getting hold of information at an early stage can certainly improve the device during the development process, thus creating the most beneficial medical technology for society. The major difference between classical and early medical HTA is that, the former supports decision-making by regulators, payers and patients about the overall value of a technology; while the latter method helps manufacturers and investors to decide about the management of the development, as well as their regulatory and reimbursement strategy. (5,6)

    Various tools are available to perform early HTA studies such as, early health economic modeling, clinical trial simulation and multi-criteria decision analysis; however, very few published articles exist to support this. In the past few years, some researchers have understood that early economic analyses/modeling during the development process help obtain optimal future results, which would help produce technologies to get market approval and reimbursement from the national health insurers.vi Another method is the ‘headroom method’, which is a relatively simple threshold approach that estimates the maximum possible cost of technology and yet still be considered cost-effective, thereby making avoiding misguided investments in those technologies that will never be cost-effective. (7) Bayesian statistics is yet another tool that has been increasingly used in health economic evaluations over the past years. It is certainly a useful tool for early HTAs, since it allows evaluations to be performed repeatedly as the knowledge base evolves. (8)

    Furthermore, clinical trial simulation (CTS) is a procedure which applies available knowledge about the technology under development using mathematical relationships and models. (9) This procedure estimates different efficiency and tolerability profiles ahead of the clinical data acquisition. Its use can therefore help manufacturers minimize the duration and costs of technology development. In addition, multi-criteria decision analysis (MCDA) is a method to support decisions between two or more discrete alternatives. (10) It helps decision-makers in data organization and transparent decision making and consists of many validated methods, including analytic hierarchy process (AHP), conjoint analysis and contingent valuation. However, AHP is the only one that has been applied in early HTAs of medical devices. Another method is value of information (VOI) analysis, which is based on the underlying principle of comparing the costs and benefits of collection of additional information. (11)

    There is a possibility that that the most appropriate early HTA approach might vary among technologies in both devices and tests. The concept of early HTA represents a new way to evaluate technologies that should receive more attention in the future. Early HTA can help reduce the time and investments required in developing new technology, and also in development of more effective and cost-effectiveness medical technologies.

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    References 

    1. Why do we need HTA? Swedish Agency for Health Technology Assessment and Assessment of Social Services. June, 2016. 
    2. Early HTA- Early Stage Health Technology Assessment. University of twente.
    3. Chapman AM. The use of early medical evaluation to inform medical device development decisions: An evaluation of the headroom method.
    4. MarkiewickzK, et al. Commercial viability of medical devices using Headroom and retunr on investment calculation. Technological Forecasting and Social Change 2016; 112:338-346.
    5. Redekop K. Early medical technology assessments of medical devices and tests. JHPOR 2013; 1:26-37.
    6. IJzerman MJ, et al. Emerging Use of Early Health Technology Assessment in Medical Product Development: A Scoping Review of the Literature. Pharmacoeconomics 2017; 35(7):727-740.
    7. Sculpher M, et al. The iterative use of economic evaluation as part of the process of health technology assessment. J Health Serv Res Policy 1997; 2:26–30.
    8. Spiegelhalter DJ, et al. Bayesian methods in health technology assessment: a review. Health Technol Assess 2000; 4(38):1-130.
    9. Girard P, et al. Clinical trial simulation in drug development. Therapie 2004; 59(3):297-304.
    10. Thokala P, et al. Multiple Criteria Decision Analysis for Health Technology Assessment. Value Health 2012; 15: 1172-1181.
    11. Rao C, et al. Value of Information Analysis. In: Athanasiou T., Debas H., Darzi A. (eds) Key Topics in Surgical Research and Methodology. 2010. Springer, Berlin, Heidelberg.

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  • Adaptive Licensing and Real World Evidence (RWE)

    Adaptive Licensing and Real World Evidence (RWE)

    We all want safe and effective medicines to reach patients as soon as possible, but as we know, drug development, market authorization and payer assessment are all slow sections of a long and drawn out journey for a drug. But what if patients could have access to medicines not just months earlier, but potentially 8 years earlier? This is exactly what the European Medicines Agency (EMA) have in mind, as they lead a broad and diverse group of key stakeholders towards a root-and-branch upheaval of current practice. Adaptive Licensing (AL) (earlier known as adaptive pathways; AP), an ambitious and evolving new initiative which incorporates Real World Evidence (RWE): clinical data collected outside of a conventional randomized controlled trial. AL reforms the existing regulatory approach.

    In March 2014 EMA launched a pilot project to explore the adaptive pathways approach, a scientific concept of medicines development and data generation intended for medicines that address patients’ unmet medical needs. AL seeks to balance timely access for patients who are likely to benefit most from the medicine with the need to provide adequate evolving information on the benefits and risks of the medicine itself. AL is not a new route of approval for medicines. It makes use of existing approval tools, in particular conditional marketing authorization, which has been in operation in the European Union (EU) since 2006. It also builds on the experience gained with strengthened post-marketing monitoring tools introduced by the 2012 pharmacovigilance legislation (e.g., post-authorization studies and patient registries). The adaptive pathways concept is not meant to be applicable to all medicines, but only to medicines that are likely to offer help for a patient population with an unmet medical need, and where the criteria for adaptive pathways apply.

    Notwithstanding the classic randomized controlled clinical trials (RCTs) are Gold Standard for the regulatory approval of new technologies, their inherent generation of efficacy and safety data, are not always utilizable in the daily context. Items such as ‘homogenous populations without other diseases than the one explored in the study’, ‘placebo comparator, not the standard treatment or other active comparator’, and ‘high adherence,’ are just to nominate some points, which are far from the regular use of a medication on the part of patients and healthcare professionals. Even though currently, in parallel with clinical studies, collection programs of observational information are more and more generated, the available evidence is limited and onerous in case of necessity of large volumes; at least by means of clinical studies. This can be overcome with the help of real-world data.

    RWE refers to the planned and systematic recollection of the data generated outside the clinical studies. Adaptive approaches link decision making to an evolving evidence base, parts of which are frequently seen as being derived from analyses of observational data gathered from sources such as electronic medical records, registries or administrative databases. Acceptance of such evidence is an important issue- regulatory authorities and payers are currently prepared to accept observational data to support manufacturers’ efficacy/effectiveness claims only in limited circumstances. In the pilot project, the concept of RWE was expressly intended as wide ranging, encompassing different types of observational research that may be utilized to supplement randomized clinical trials. This was to encourage the submission of different approaches, not all of which could be foreseen at the conceptual stage, with the intent to highlight possibilities, needs and maximize the learning potential.

    RWE data collection within AL has the potential to improve our understanding of disease processes, epidemiological factors, and difficult issues such as adherence, which will in turn allow RCTs to become more efficient. Additionally, for many subpopulations, the life span approach to licensing and coverage and learning from real-world experience as advocated by adaptive pathways will become the only viable access route to new treatments in future. Additionally, in-depth knowledge of the natural history of diseases, existing baseline data, as well as other epidemiology aspects gleaned from existing databases or emerging large data networks and reanalysis of past trials helps to make RCTs more efficient and identify surrogate endpoints, and may increasingly obviate the need for concurrent control groups, e.g., in rare diseases. This knowledge and data can also be leveraged for the post-initial licensing evidence generation foreseen under AL, by providing a reference point against which the real-world performance of a treatment can be assessed.

    Further important steps towards enabling AL are currently being taken. Regulators have just begun to explicitly address and communicate “uncertainty” in their templates for benefit–risk assessment. A growing number of regulators and payer (or HTA) organizations involve patients in their decision-making processes. This can be considered as a pertinent analogy for the history of bringing new drugs to market.

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  • EBM and HTA for Healthcare Decision Making – The Time has Come!

    EBM and HTA for Healthcare Decision Making – The Time has Come!

    Health systems have developed at different speeds, and with differing degrees of complexity throughout the twentieth century, reflecting the diverse political and social conditions in each country. Notwithstanding their diversity, all systems, however, share a common reason for their existence, namely the improvement of health for their entire populations. To attain this goal a health system undertakes a series of functions, most notably, the financing and delivering of health services.

    Since available resources are limited, delivering health services involves making decisions. Decisions are required on what interventions should be offered, the way the health system is organized, and how the interventions should be provided in order to achieve an optimal health gain with available resources, while, at the same time, respecting people’s expectations. Decision-makers thus need information about the available options and their potential consequences. It is now clear that interventions once thought to be beneficial have, in the light of more careful evaluation, turned out to be at best of no benefit or, at worst, harmful to the individual and counterproductive to the system. This recognition has led to the emergence of a concept known as “evidence-based medicine” (EBM), which argues that the information used by policymakers should be based on rigorous research to the fullest extent possible.

    Health technology assessment (HTA) increasingly plays an important role in informing reimbursement and pricing decisions and providing clinical guidance on the use of medical technologies across the world. In addition to safety and efficacy information, health economic and outcomes research (HEOR) data are also receiving expanded attention in these assessments in many countries, due to payers seeking better value for money spent on treatments. HTA is now commonly viewed as a tool to assist evidence-based health-care decisions.

    EBM has been defined as “the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients”. The origin of this evidence-based approach can be seen in the application of clinical medicine delivered at an individual level. Pressure to base decisions on evidence has, however, been extended to other areas of health care, such as public health interventions and health care policy-making. In this context, evidence is understood as the product of systematic observation or experiment. It is inseparable from the notion of data collection. The evidence-based approach relies mainly on research, that is, on systematically collected and rigorously analyzed data following a pre-established plan.

    There are exciting new developments in basic science that could lead to targeted, highly effective and curative treatments. Health systems are improving their electronic records and recording health outcomes, which can be analyzed using structured, sophisticated analyses in real-time. There are also new collaborative approaches between healthcare providers and technology developers to enable evaluation of technologies in the health system before adoption or early in adoption to optimize use. There is a need and an opportunity to harness these developments and improve the effectiveness and efficiency of evidence production for new health technologies to input to HTA and inform decision making. Clinicians, managers, patients, and technology developers need to be involved to ensure that the process to a coverage decision is not only efficient but that it is also effective. To be effective, health services need to be organized to enable rapid and appropriate introduction of effective technologies and disinvestment of ineffective technologies. This suggests an additional responsibility for HTA and it would involve helping technology developers understand clinical and patient needs, evidence generation requirements, and limitations and helping health systems understand the potential and implications of new technologies and possible challenges of implementation.

    Therefore, to sum everything up, the evidence should be both efficient as well as effective in order to develop more agile and adaptive processes that help to broker alignment among technology developers and health systems (including healthcare professionals and patients). This suggests that HTA needs to innovate and be prepared to play a more active role to influence evidence production and help facilitate dialogue among stakeholders to optimize technology development and use.

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