#HEORInsights

In the ever-evolving landscape of healthcare, Real-World Data (RWD) studies have emerged as a pivotal tool in shaping treatment strategies and enhancing patient outcomes. For the Health Economics and Outcomes Research (HEOR) industry, these studies hold a special significance, providing valuable insights into the real-world effectiveness of treatments. In recent times, synthetic controls in oncology RWD studies have gained momentum, offering a novel approach to accelerate the development of new treatments and broaden our understanding of their impacts.[1]

The essence of oncology research lies in its constant pursuit of more effective and targeted treatments for a range of malignancies. Traditional Randomized Clinical Trials (RCTs), while crucial, often have limitations that restrict their ability to mirror real-world scenarios comprehensively. This is where RWD studies step in, utilizing data collected from routine clinical practice to bridge the gap between RCTs and real-life patient experiences. However, challenges such as confounding variables and lack of randomization persist in these studies, prompting the exploration of innovative methodologies like synthetic controls.[1,2]

Synthetic controls, in essence, involve the creation of a hypothetical control group that mirrors the characteristics of the treatment group. By leveraging historical patient data, demographic information, disease progression, and other relevant factors, researchers can construct a comparable control arm. This approach, rooted in advanced statistical techniques, provides a powerful tool to estimate treatment effects and mitigate biases that might arise in traditional observational studies.[5]

Synthetic control arms are a variant of external control arms, and represent an inventive strategy where researchers create a virtual or synthetic control group by harnessing existing data, rather than enlisting fresh participants for the control cohort. The formulation of a synthetic control arm entails evaluating patient information contained within pre-existing datasets, like electronic health records, which is rendered anonymous and stripped of any personally identifiable details. These synthetic controls replicate real patients who would conventionally be enrolled as part of the trial’s control group.[5]

The application of synthetic controls in oncology RWD studies offers several key advantages to the HEOR industry. It expedites the evaluation of new treatments by reducing the time required for traditional RCTs. This acceleration is paramount in oncology, where swift access to effective treatments can significantly impact patient outcomes and quality of life. Moreover, synthetic controls enable researchers to glean insights from real-world patient populations that might have been excluded from traditional clinical trials due to stringent eligibility criteria. This inclusivity not only enhances the generalizability of study findings but also provides a more holistic understanding of treatment efficacy across diverse patient demographics. Next, by harnessing the richness of RWD, synthetic controls facilitate the assessment of treatment effects in various subpopulations, shedding light on the intricate interplay between treatments and patient characteristics.[2-5]

As the HEOR industry delves deeper into the utilization of synthetic controls for oncology RWD studies, it is imperative to acknowledge the challenges that accompany this innovative approach. Rigorous validation and robust sensitivity analyses are paramount to ensure the credibility of synthetic control results. Transparency in methodology and data sources is equally vital to establish trust among stakeholders and foster the adoption of this methodology in regulatory decision-making.[3]

In conclusion, the integration of synthetic controls in oncology RWD studies presents a promising avenue for the HEOR industry to enhance treatment development and expedite knowledge acquisition. This methodology’s ability to replicate a control group closely resembling the treatment group regarding relevant variables addresses some of the limitations inherent in observational studies. As the healthcare landscape continues to evolve, embracing innovative methodologies like synthetic controls has become necessary to drive advancements in oncology treatments and ultimately improve patient outcomes.

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References

1. Prasad V. Reliable, cheap, fast and few: What is the best study for assessing medical practices? Randomized controlled trials or synthetic control arms?. European journal of clinical investigation. 2021 Aug 1;51(8):e13580.
2. Yap TA, Jacobs I, Baumfeld Andre E, et al. Application of Real-World Data to External Control Groups in Oncology Clinical Trial Drug Development. Front Oncol. 2022 Jan 6;11:695936.
3. Thorlund K, Dron L, Park JJH, Mills EJ. Synthetic and External Controls in Clinical Trials – A Primer for Researchers. Clin Epidemiol. 2020 May 8;12:457-467.
4. Greshock J, Lewi M, Hartog B, Tendler C. Harnessing real-world evidence for the development of novel cancer therapies. Trends in Cancer. 2020 Nov 1;6(11):907-9.
5. Banerjee R, Midha S, Kelkar AH, Goodman A, Prasad V, Mohyuddin GR. Synthetic control arms in studies of multiple myeloma and diffuse large B-cell lymphoma. British journal of haematology. 2022 Mar 1;196(5):1274-7.