The Pros And Cons Of Synthetic Control Arms In Clinical Trials

By Harriet Gray Stephens, MFPM, medical director, Boyds

Synthetic control arms (SCAs) are an innovative approach that is increasingly being adopted in clinical trials and a type of external control arm study. External controls have been defined by the FDA as any control group that is not a part of the same randomized study as the group receiving the investigational therapy. SCAs are generated using statistical methods applied to one or more data sources, such as the results of separate clinical trials or real-world data (RWD). The data is imputed to make it comparable to the intervention population within the clinical trial.

SCAs are especially useful when conducting traditional randomized controlled trials (RCTs) with a placebo or standard-of-care arm. While RCTs remain the gold standard for evaluating the safety and efficacy of new medical treatments, maintaining a concurrent control arm is sometimes unethical, impractical, or infeasible and can lead to increased patient burden and threaten the completion of a trial. Using an SCA provides supportive evidence, contextualizing the treatment effect and safety profile in situations where this information would not otherwise be available.

An SCA provides an alternative to uncontrolled or crossover clinical trials. Uncontrolled trials are commonly used in orphan diseases, where there is a shortage of patients, or very serious disease conditions without other treatments where there are scientific or ethical concerns about treatment switching or disease progression that would contraindicate the use of crossover trial designs. While uncontrolled trials can produce important safety and efficacy data, there is a significant risk of generating biased data because of a lack of randomization and a control arm to understand alternative treatment options or baseline disease states.

What Are The Ideal Data Sources For An SCA?

SCAs are constructed using patient-level data, obtained from patients not involved in the investigational clinical trial. Patients are “matched” using statistical or analytical methods to achieve balanced baseline features such as demographics and disease composition to generate an SCA that closely matches the experimentally treated patients. This enables a direct comparison of the SCA with the investigational arm. Sponsors should design the SCA early in protocol development and implement it when the clinical trial protocol has been finalized. They should initiate recruitment to enable the matching of patients while avoiding biases arising from manipulating the clinical trial protocol to match the data available to generate the SCA.

There is no formal restriction on where and in what format data comes, provided it meets the required quality criteria. Primarily, data arises from large data sets of historical clinical trials and real-world data (RWD). This is one of the key advantages of an SCA: By combining multiple data sources including historical literature comparisons, real-world data, and clinical trial data, organizations can generate excellent participant matching, develop more precise estimates of the comparison group outcome, and explore subgroup effects within the synthetic control population.

There are advantages and disadvantages of using clinical trial data compared to RWD. Clinical trial data is generally lower volume but highly standardized and has good quality. However, clinical trial data may not represent the patient population owing to recruitment biases including under-representation of certain ethnic, socio-economic, or age groups. RWD is higher volume data but is often composed of multiple sources and with worse standardization. This can make its use more difficult or more resource-intensive as more data processing is required to standardize the data. Additionally, RWD is more likely to have missing data, so organizations must carefully consider whether this makes that patient’s data set unusable or how to impute for minor missing data.

Once the appropriate data has been processed and data sources are selected, data matching of the synthetic control individuals with the investigational participants can occur. Multiple statistical methods exist to match the individuals including propensity scoring methods. There is significant regulatory interest in these methods. Indeed, selecting a method acceptable to regulatory authorities is key, and early regulatory discussions regarding these methods are recommended to achieve approvals.

What Regulators Think About SCAs

In 2001, the FDA accepted the use of external controls, where justified, to support regulatory decisions, stating in its latest guidance (February 2023) that external controls, such as SCAs, should be considered on a case-by-case…