Over the past decade, single arm trials have emerged as an accepted way of assessing a new treatment intervention. Single arm trials establish clinical benefit by demonstrating the positive effects of a new therapy or treatment, without the need to use placebo or standard of care as a control. Instead, alternative approaches of establishing the comparison are used; these have become known as external controls or synthetic control arms (SCA henceforth) and include approaches leveraging real world data from various sources or evaluations of historical clinical trial data.
Although synthetic control arm studies have a lot of promise, there are often several misconceptions about them in the industry. In a recent Cytel webinar, our panel of expert speakers from Roche, CIOX Health and Cytel, share their insights and bust industry myths around the use of synthetic controls in research and regulatory applications. In this four-part blog series, we will dispel these myths for you, beginning with the myth surrounding SCA’s regulatory acceptance.
Myth 1: SCAs get easy acceptance from regulatory agencies and HTAs
In the case of certain cancers and rare diseases, enrolling a control group is often difficult and randomizing to control is next to impossible because the disease is rare, and it is challenging to recruit patients. A lot of manufacturers are now looking at single arm trials to develop the urgently needed new medicines for these diseases.
Several publications and webinars talk about how single arm trials can get us regulatory approvals and reimbursements quite simply and easily. But is that factually correct?
Dr. Sreeram Ramagopalan’s team at Roche looked at some HTA submissions where synthetic control arms were used in single arm trials. Despite all the buzz around SCA, the real world data that was submitted as SCA was almost universally rejected by the HTAs. The HTAs feel that this data does not help them in decision-making, and they had two key pieces of feedback:
- Unmeasured confounding - one arm came from a real-world data study and the other from a trial; therefore, the propensity score method may not address differences in study designs.
- Missing data - the omission of important variables from the matching process, which may confound the treatment effect estimates obtained
Dr. Ramagopalan however believes that there are opportunities to work together, across stakeholders to address these kinds of concerns. We need to come to an agreement on the quality and reliability of real world data sources to inform decision making. Decision makers also need to actively explore real world data sources in other countries, especially in the case of small patient populations with rare conditions.
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About the Author of Blog:
Mansha Sachdev specializes in content creation and knowledge management. She holds an MBA degree and has 11 years of experience in handling various facets of marketing, across industries. At Cytel, Mansha is a Content Marketing Manager and is responsible for producing informative content that is related to the pharmaceutical and medical devices industries.
