Validate Real-World Data-based Endpoint Measures of Cancer Treatment Outcomes

Abstract

Recently, there is a growing interest in using real-world data (RWD) to generate real-world evidence (RWE) that complements clinical trials. Nevertheless, to quantify the treatment effects, it is important to develop meaningful RWD-based endpoints. In cancer trials, two real-world endpoints are particularly of interest: real-world overall survival (rwOS) and real-world time to next treatment (rwTTNT). In this work, we identified ways to calculate these real-world endpoints with structured EHR data, and validated these endpoints against the gold-standard measurements of these endpoints derived from linked EHR and TR data. In addition, we also examined and reported the data quality issues especially the inconsistency between the EHR and TR data. Using survival model, our result showed that patients (1) without subsequent chemotherapy or (2) with subsequent chemotherapy and longer rwTTNT, would have longer rwOS, showing the validity of using rwTTNT as a real-world surrogate marker for measuring cancer endpoints.