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  • Hybrid Study Designs: Combining Existing and New Data for Valuable Insights

    Hybrid Study Designs: Combining Existing and New Data for Valuable Insights
    Hybrid Study Designs

    Hybrid study designs are research methodologies that seamlessly blend elements from multiple study designs to address specific research questions comprehensively. Such a hybrid approach allows researchers to capitalize on the strengths of each study design while mitigating their individual limitations. As a result, hybrid designs usually provide a more comprehensive and holistic view of the research question.(1, 2)

    Hybrid study designs come in various forms, each tailored to specific research needs. One example for hybrid study designs are ambispective studies, which combine retrospective and prospective data, enabling the evaluation of research questions before and after specific episodes, making it particularly valuable for studying intervention impacts or rare events.(3, 4)

    The hybrid study design strategy has been used to enhance the robustness of traditional clinical trials, resulting in the evolution of hybrid clinical trials. While traditional clinical trials have long been the gold standard for generating evidence to support the safety and efficacy of new interventions, they have faced limitations in capturing the complexities of everyday practice.  Hybrid trials aim to generate evidence that considers both the controlled rigor of an RCT and the real-world applicability of observational studies. They can provide insights that are relevant to both research and clinical practice and can embrace a more inclusive approach. Additionally, with access to retrospective data, hybrid trials can evaluate interventions over extended periods, providing valuable insights into the long-term impact of treatments and interventions on patient outcomes.(5)

    Among hybrid clinical trials, one notable subtype is the Pragmatic Randomized Clinical Trial (RCT), which focuses on evaluating interventions under real-world conditions. By prioritizing applicability to routine clinical practice, pragmatic RCTs generate evidence that is directly relevant to everyday healthcare settings. This approach enables researchers to assess whether an intervention works in practical clinical scenarios, providing evidence that directly informs decision-making in clinical practice and healthcare policy. Embedded Pragmatic Randomized Trials take the concept of a pragmatic RCT even further. In an embedded trial, the intervention being studied is seamlessly integrated into routine patient care within the existing healthcare system. This integration ensures minimal disruptions to routine care for both healthcare providers and patients, making the trial highly feasible and practical.(6, 7)

    There are other examples for hybrid clinical trials. The ‘Stepped-Wedge Cluster Randomized Trial’ design evaluates new interventions over time by sequentially randomizing different clusters to receive the intervention. The ‘Observational Study With Nested Randomized Trial’ design identifies participants from an observational study for an RCT exploring specific interventions within an existing cohort. The ‘Sequential Multiple Assignment Randomized Trial (SMART)’ randomizes participants to different treatments in multiple stages based on previous responses. These various designs offer flexibility and adaptability to address diverse research questions effectively.(8-10)

    However, implementing hybrid study designs also presents some challenges. Integrating multiple data sources and research methods requires careful planning and coordination. Researchers must ensure the compatibility and coherence of the different data sets, establish rigorous data management procedures, and address potential biases or conflicts that may arise from the combination of data. Additionally, researchers need expertise in quantitative and qualitative methods to navigate the complexities of hybrid designs effectively.(2, 3)

    In conclusion, hybrid designs represent remarkable steps forward in evidence generation. By merging existing data with newly collected information, these methodologies provide a holistic view of research questions, enabling researchers to uncover valuable insights and long-term trends. As healthcare continues to evolve, hybrid designs and hybrid clinical trials will play pivotal roles in bridging the gap between research and clinical practice, shaping a future where evidence is not only robust but also reflective of the diverse and ever-changing realities of patient care.

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    References:

    1. Soon GG, Nie L, Hammerstrom T, et al. Meeting the demand for more sophisticated study designs. A proposal for a new type of clinical trial: the hybrid design. BMJ Open. 2011 Jan 1;1(2):e000156.
    2. Landes SJ, McBain SA, Curran GM. An introduction to effectiveness-implementation hybrid designs. Psychiatry Res. 2019 Oct;280:112513.
    3. Curran GM, Bauer M, Mittman B, et al. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012 Mar;50(3):217-26.
    4. Sessler DI, Imrey PB. Clinical research methodology 2: observational clinical research. Anesthesia & Analgesia. 2015 Oct 1;121(4):1043-51.
    5. Zhu M, Sridhar S, Hollingsworth R, et al. Hybrid clinical trials to generate real-world evidence: design considerations from a sponsor’s perspective. Contemp Clin Trials. 2020 Jul;94:105856.
    6. Koehler M, Donnelly ET, Kalanovic D, et al. Pragmatic randomized clinical trials: a proposal to enhance evaluation of new cancer therapies with early signs of exceptional activity. Annals of Oncology. 2016 Jul 1;27(7):1342-8.
    7. Ramsberg J, Platt R. Opportunities and barriers for pragmatic embedded trials: triumphs and tribulations. Learning Health Systems. 2018 Jan;2(1):e10044.
    8. Creswell JW, Clark VP. Mixed methods research. SAGE Publications. 2011.
    9. Hemming K, Haines TP, Chilton PJ, et al. The stepped wedge cluster randomized trial: rationale, design, analysis, and reporting. BMJ. 2015 Feb 6;350.
    10. Digitale JC, Stojanovski K, McCulloch CE, Handley MA. Study designs to assess real-world interventions to prevent COVID-19. Frontiers in public health. 2021 Jul 27;9:657976.

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  • Evaluating Medical Devices: How Do RCT Data and RWD Fit Together?

    Evaluating Medical Devices: How Do RCT Data and RWD Fit Together?
    The banner image talks about evaluation of medical devices using RCT and RWD

    Medical devices play a critical role in modern healthcare, aiding in diagnosing, treating, and managing various diseases and conditions. Before these devices are approved for widespread use, they undergo rigorous evaluation to ensure their safety and effectiveness. Two key sources of data used in the evaluation process are Randomized Control Trial (RCT) data and Real-World Data (RWD). While RCTs provide controlled and standardized evidence, RWD reflects the device’s performance in everyday clinical practice. Understanding how these two sources of data complement each other is essential for making informed decisions about medical device adoption and patient care.(1)

    RCTs are widely regarded as the gold standard for assessing the safety and efficacy of medical devices. These trials involve carefully designed studies where participants are randomly assigned to different treatment groups. This randomization minimizes bias and allows for the comparison of treatment outcomes. RCTs follow predefined protocols and often have strict inclusion and exclusion criteria, which helps ensure a homogeneous study population.(2,3)

    RCT data provides valuable insights into the specific device being evaluated. Researchers meticulously collect and analyze data, monitoring participant outcomes, adverse events, and treatment response. The controlled environment of RCTs allows for a detailed examination of the device’s performance, enabling researchers to draw reliable conclusions about its effectiveness. Furthermore, the blinding and randomization processes in RCTs help minimize confounding factors and enhance the internal validity of the findings.(3)

    While RCTs provide vital initial evidence, RWD offers a complementary perspective on medical device performance. RWD is collected from routine clinical practice and reflects the device’s use in diverse patient populations, various healthcare settings, and in the presence of coexisting conditions. This data is obtained from electronic health records, claims databases, registries, and other sources, providing information about a device’s long-term effectiveness and safety.(4)

    RWD captures the complexity of patient care and provides insights into how a device performs in everyday clinical practice. It helps identify potential issues that may not have emerged during the controlled setting of RCTs. RWD can uncover rare adverse events, treatment challenges in specific patient subgroups, and the durability of device efficacy over time. Additionally, it enables the evaluation of device performance in real-world settings, where factors such as operator experience and patient adherence come into play.(4,5)

    While RCTs and RWD have distinct strengths, combining these sources of information can enhance our understanding of medical device performance. RCT data establishes the initial evidence base and provides critical insights into the device’s efficacy under controlled conditions. This data is often used to support regulatory approval and guide initial clinical decisions.(4-6)

    RWD, on the other hand, supplements RCT findings by providing a broader view of device performance in diverse patient populations and real-world clinical settings. It can help identify potential risks or benefits that may not have been apparent during the controlled environment of an RCT. Moreover, RWD can inform post-market surveillance efforts, aiding in continuously monitoring device safety and effectiveness.(4)

    The integration of RCT data and RWD can help address limitations inherent in each dataset. For example, while RCTs may have limited sample sizes and shorter follow-up periods, RWD can provide insights into long-term outcomes and rare adverse events. Conversely, RWD may suffer from biases, such as confounding variables or incomplete documentation, which can be mitigated by the controlled design of RCTs.(2)

    Evaluating medical devices requires a comprehensive approach that combines the strengths of both RCT data and RWD. RCTs establish the initial evidence base, while RWD provides insights into the device’s performance in diverse patient populations and everyday clinical practice. The synergy between these two data sources is crucial for informed decision-making regarding medical device adoption, patient care, and post-market surveillance. By embracing a holistic approach to data evaluation, we can maximize the benefits and safety of medical devices, ultimately improving patient outcomes.

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    References:

    1. Ming J, He Y, Yang Y, et al. Health technology assessment of medical devices: current landscape, challenges, and a way forward. Cost Effectiveness and Resource Allocation. 2022 Dec;20(1):1-9.
    2. Tarricone R, Boscolo PR, Armeni P. What type of clinical evidence is needed to assess medical devices? Eur Respir Rev. 2016 Sep;25(141):259-65.
    3. Tarricone R, Callea G, Ogorevc M, Prevolnik Rupel V. Improving the methods for the economic evaluation of medical devices. Health Economics. 2017 Feb;26:70-92.
    4. U.S. Food and Drug Administration. FDA. Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices. 2023; Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-real-world-evidence-support-regulatory-decision-making-medical-devices.
    5. Dhruva SS, Ross JS, Desai NR. Real-world evidence: promise and peril for medical product evaluation. Pharmacy and Therapeutics. 2018 Aug;43(8):464.
    6. Valla V, Tzelepi K, Charitou P, et al. Use of Real-World Evidence for International Regulatory Decision Making in Medical Devices. International Journal of Digital Health. 2023 Jan 1;3(1):1.

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