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Synthetic and External Control Arms

"While the implementation of [SCAs] for regulatory purposes might be a new development, the majority of statistical and mathematical theories used for the design of SCAs are decades old and familiar to the scientific community."

- From Demystifying Synthetic Control Arms: The Ebook

The increased use of single arm trials  in oncology and rare diseases creates challenges when comparing treatments to one another. Synthetic and external control arms are used to augment single arm clinical trial data.

These methods are driven by data access improvements alongside the leveraging of statistical toolkits from many research fields.

Using real world analytics (RWA) solutions and core principles from epidemiology, we select the optimal historical clinical data for RWA synthetic control arms. Our statisticians and data scientists then employ a number of advanced methods including Bayesian dynamic borrowing, propensity scoring, quantitative bias assessment, and others to ensure that historical data rigorously augments your trial.

Our forecasting engines can tell you what proportion of your comparator arm should utilize historical data, helping you to optimize allocation of patients. This also ensures that sponsors don't needlessly conduct experiments on patients for marginal increases in scientific rigour.

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