Historically, getting patients in clinical trials is the biggest challenge to healthcare breakthrough. But now data can used to replace patients in clinical trials. A key priority for the life sciences and pharmaceutical industry is to speed up clinical trials in order to bring treatments to market faster. But modernizing clinical trials is easier said than done, with the large number of participants needed for trials and participants’ fears of being assigned to placebo (‘fake’ treatment) proving to be a challenge. This can be eased by using an innovative approach to collect comparison data, the synthetic control arm (SCA). In order to understand this phenomena in detail, BioSpectrum Asia got in touch with Dr Xiang Yin, a Senior Lead Biostatistician at Medidata Solutions (Acorn AI). Medidata Solutions is an American technology company that develops and markets software as a service for clinical trials.
Edited Excerpts-
What is the basic concept behind ‘synthetic control arm’?
Unlike a traditional randomized clinical trial (RCT) which collects control data from patients who have been assigned to a concurrent control, a synthetic control arm (SCA) utilizes historical clinical trials and applies statistical methodology to select historical patients who before study treatment are the same as the baseline condition of patients enrolled into a single-arm trial or assigned to the experimental arm in a RCT with a compromised control arm.
In instances of a serious medical condition for which a new medical product may fulfill an unmet medical need, the US FDA may grant accelerated approval of that product based on early evidence of efficacy from a surrogate endpoint (e.g., tumor shrinkage) with confirmatory evidence of clinical benefit (e.g., increased mortality) required after marketing begins and usually provided with randomized controlled phase 4 studies. At the time of accelerate approval, at least a suggestion of efficacy is present for these products and in a setting of unmet medical need they are generally sought after by patients even before the confirmatory trial is complete. As such, accelerated approvals allow patients early access to new therapies.
However, the accelerated approval can make it difficult to complete the confirmatory trial. Since the drug has preliminary evidence of efficacy and since it is available in market, trial subjects who are randomized to the control arm may instead choose to drop out and seek the treatment themselves. With a tendency of higher drop out rate on the control arm, or increased cross-over incidences from control to treatment arm, this can result in a disproportionate number of patients and follow-up time on control and treatment arm, which violates the statistical assumptions for proper analysis and meaningful interpretations of the results. Historical trial data are more robust and reliable in the sense that patients did not have the experimental treatment or even similar treatment options were likely not available at the time of the historical trial since they were conducted before the accelerated approval in an area of unmet need.
In addition, single arm trials are often seen in some clinical settings (e.g. rare disease and pediatric studies) where randomized control is not feasible due to small disease population and/or ethical concerns. Leveraging historical clinical trial data, especially patient-level data, can provide a valuable comparator to the experimental therapy.
With the implementation of an SCA that is built from historical trial data, this can potentially provide a control for comparison to the single arm trials (e.g. many early phase oncology trials), augment existing concurrent controls or replace the compromised controls in confirmatory trials in a way mimicking RCTs.
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