<p>Synthetic and External Controls in Clinical Trials – A Primer for Researchers</p>

Abstract

There has been a rapid expansion in the use of non-randomized evidence in the regulatory approval of treatments globally. An emerging set of methodologies have been utilized to provide greater insight into external control data used for these purposes, collectively known as synthetic control methods. Through this paper, we provide the reader with a set of key questions to help assess the quality of literature publications utilizing synthetic control methodologies. Common challenges and real-life examples of synthetic controls are provided throughout, alongside a critical appraisal framework with which to assess future publications.

Use of Real‐World Data to Emulate a Clinical Trial and Support Regulatory Decision Making: Assessing the Impact of Temporality, Comparator Choice, and Method of Adjustment

“…While the use of external comparator groups has been used to supplement findings of single‐arm trials, their use has been largely limited to rare diseases where treatment effects are relatively large and the clinical inferences relatively resistant to bias. Existing methods for nonrandomized studies need to undergo prospective and transparent validation before they can be used to support regulatory decision making 32–34 . This methodological study assessed the impact of selecting comparator groups using real‐world data by assessing the effects of temporality, comparator choice, and confounding control using variations of propensity score‐based methods.…”

“…Existing methods for nonrandomized studies need to undergo prospective and transparent validation before they can be used to support regulatory decision making. [32][33][34] This methodological study assessed the impact of selecting comparator groups using real-world data by assessing the effects of temporality, comparator choice, and confounding control using variations of propensity score-based methods. Recommendations are presented in Table 5.…”

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

“…Synthetic and external controls in clinical trials are becoming increasingly popular (Thorlund et al, 2020). Synthetic controls refer to cohorts that have been composed from real observational cohorts or EHR using statistical methodologies.…”

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

“…Synthetic controls refer to cohorts that have been composed from real observational cohorts or EHR using statistical methodologies. While the individuals included in the cohorts are usually left unchanged, micro-simulations of disease progression at the patient level are used to explore long-term outcomes and help in the estimation of treatment effects (Thorlund et al, 2020;Etzioni et al, 2002). Synthetic data generated by GANs could be transformative for the problem of finding control cohorts.…”