Addressing challenges with real-world synthetic control arms to demonstrate the comparative effectiveness of Pralsetinib in non-small cell lung cancer

Abstract

As advanced non-small cell lung cancer (aNSCLC) is being increasingly divided into rare oncogene-driven subsets, conducting randomised trials becomes challenging. Using real-world data (RWD) to construct control arms for single-arm trials provides an option for comparative data. However, non-randomised treatment comparisons have the potential to be biased and cause concern for decision-makers. Using the example of pralsetinib from a RET fusion-positive aNSCLC single-arm trial (NCT03037385), we demonstrate a relative survival benefit when compared to pembrolizumab monotherapy and pembrolizumab with chemotherapy RWD cohorts. Quantitative bias analyses show that results for the RWD-trial comparisons are robust to data missingness, potential poorer outcomes in RWD and residual confounding. Overall, the study provides evidence in favour of pralsetinib as a first-line treatment for RET fusion-positive aNSCLC. The quantification of potential bias performed in this study can be used as a template for future studies of this nature.

Abstract

Real-world data (RWD) based control arms provide an option to compare the effectiveness of single-arm trials. By performing multiple quantitative bias analyses to alleviate concerns about trial-RWD comparability, here the authors show that the RET inhibitor pralsetinib provides survival benefit in patients with RET fusion-positive non-small cell lung cancer from the ARROW single-arm trial, (NCT03037385) when compared to pembrolizumab monotherapy and pembrolizumab with chemotherapy RWD cohorts.