Real returns from synthetic control arms

Real returns from synthetic control arms

Real returns from synthetic control arms 558 344 Exploristics

By Aiden Flynn, Exploristics CEO

Drug developers running clinical trials face many challenges. However, a common pain point is recruitment. This can particularly bite when the studies involved are evaluating therapies for diseases where there are small populations or where there isn’t an approved treatment so placebo-controlled trials would be a traditional approach. Rare or orphan diseases often face both challenges.

Easing the Pain

One growing approach to addressing such development headaches is the collection and use of external comparison data to provide synthetic control arms for studies where populations are limited, or conventional designs are unfeasible. Data for these can come from disease registries, historical clinical trial data and real-world data sources and offer a practical way of augmenting the data you can collect in your study with further suitable data that is already in the public domain.

Regulatory documents like the FDA’s framework for its real-world evidence program demonstrate that Regulators are now recognising the unmined value locked up in vast and growing real-world resources that can be used to improve current clinical development approaches. This means that there has never been a better time to dip a toe into innovative waters to discover how synthetic control arms can help to facilitate studies where there are small populations.

Innovating to Gain

It’s said that fortune favours the brave, and synthetic control arms offer multiple gains for drug developers who have the courage to innovate. With the support of statisticians, they can be used as part of a hybrid trial design, reducing recruitment pressures significantly by decreasing the need to enrol control participants. In this way, their use in studies can:

  • Increase efficiency
  • Decrease timelines
  • Drive down costs
  • Accelerate delivery of medicines to market
  • Turn infeasible studies into practical and viable approaches

Hybrid trial designs can be a low-risk, high-reward step for sponsors to integrate the use of real-world data into their development approach and can help to inform go/no go development decisions, decreasing the risk of late-stage failure. Moreover, the use of synthetic control arms can help improve participation. In cases where patients have a poor prognosis and the current standard of care is not very effective (or non-existent), they can be used as the study control arm, so patients recruited to the study are more likely to receive active treatment. However, input from statisticians in such cases is crucial to reduce any associated risk of bias being introduced into the study as a result.

Insights that can help reap more ROI

Synthetic control arms have been successfully used in studies to support accelerated approval by the FDA and EMA. However, despite such regulatory precedent and their potential to alleviate key pain points for developers, their use is still not yet widespread. Certainly, they are not appropriate for every study and cannot be used to replace control arms wholesale or to cut corners when reasonable alternatives are available. Perhaps, part of the lack of uptake may be due to the extra steps involved in identifying and extracting pertinent real-world data as well as cleaning and aligning it to ensure that it is of suitable quality to be used as an external control. While, this can be off-putting initially, with the right process in place, these challenges are not insurmountable. Here, engaging with statisticians and data scientists early in the clinical trial design process can help create a framework to make this possible. Investing a little time and money upfront to assess if a synthetic control arm is feasible can deliver greater savings down the line in terms of reducing recruitment pressures and costs.

Data scientists now offer new tools and methods that can identify and extract information from messy real-world data to deliver structured, research-grade data. Statisticians can then assess the feasibility of its use with regards to endpoints, missingness and build a process to reduce the risk of bias. With this kind of expertise on your clinical trial design team, you can design the right study that mitigates common risks with using synthetic control arms, smoothing regulatory engagement. Once in place, the use of synthetic control arms can be integrated into your ‘study design option suite’, overcoming recruitment issues, accelerating development plans to help improve return on R&D investment.


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It’s not rare to have a rare disease

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