Overcoming the discrepancies between RCTs and real-world data by accounting for Selection criteria, Operations, and Measurements of Outcome (SOMO)

The potential of real-world data to inform clinical trial design and supplement control arms has gained much interest in recent years. The most common approach relies on reproducing control arm outcomes by matching real-world patient cohorts to clinical trial baseline populations. However, recent studies pointed out that there is a lack of replicability, generalisability, and consensus. Further, few studies consider differences in operational processes. Discovering and accounting for confounders, including hidden effects related to the treatment process and clinical trial study protocol, would potentially allow for improved translation between clinical trials and real-world data. In this paper, we propose an approach that aims to explore and examine these confounders by investigating the impact of selection criteria and operations on the measurements of outcome. We tested the approach on a dataset consisting of small cell lung cancer patients receiving platinum-based chemotherapy regimens from a real-world data cohort (n=223) and six clinical trial control arms (n=1,224). The results showed that the discrepancy between real-world and clinical trial data potentially depends on differences in both patient populations and operational conditions (e.g., frequency of assessments, and censoring), for which further investigation is required. The outcomes of this work suggest areas of improvement for systematically exploring and accounting for differences in outcomes between study cohorts. Continued development of the method presented here could pave the way for transferring learning across clinical studies and developing mutual translation between the real-world and clinical trials to inform clinical study design.