Matching by OS Prognostic Score to Construct External Controls in Lung Cancer Clinical Trials

Abstract

External controls (eControls) leverage historical data to create non‐randomized control arms. The lack of randomization can result in confounding between the experimental and eControl cohorts. To balance potentially confounding variables between the cohorts, one of the proposed methods is to match on prognostic scores. Still, the performance of prognostic scores to construct eControls in oncology has not been analyzed yet. Using an electronic health record‐derived de‐identified database, we constructed eControls using one of three methods: ROPRO, a state‐of‐the‐art prognostic score, or either a propensity score composed of five (5Vars) or 27 covariates (ROPROvars). We compared the performance of these methods in estimating the overall survival (OS) hazard ratio (HR) of 11 recent advanced non‐small cell lung cancer. The ROPRO eControls had a lower OS HR error (median absolute deviation (MAD), 0.072, confidence interval (CI): 0.036–0.185), than the 5Vars (MAD 0.081, CI: 0.025–0.283) and ROPROvars eControls (MAD 0.087, CI: 0.054–0.383). Notably, the OS HR errors for all methods were even lower in the phase III studies. Moreover, the ROPRO eControl cohorts included, on average, more patients than the 5Vars (6.54%) and ROPROvars cohorts (11.7%). The eControls matched with the prognostic score reproduced the controls more reliably than propensity scores composed of the underlying variables. Additionally, prognostic scores could allow eControls to be built on many prognostic variables without a significant increase in the variability of the propensity score, which would decrease the number of matched patients.