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

Recently, there has been a growing interest in using real-world data (RWD) to generate real-world evidence that complements clinical trials. To quantify treatment effects, it is important to develop meaningful RWD-based endpoints. In cancer trials, two real-world endpoints are of particular 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 electronic health record (EHR) data and validate these endpoints against the gold-standard measurements of these endpoints derived from linked EHR and tumor registry (TR) data. In addition, we examined and reported data quality issues, especially inconsistencies between the EHR and TR data. Using a survival model, we show that the presence of next treatment was not significantly associated with rwOS, but patients who had longer rwTTNT had longer rwOS, validating the use of rwTTNT as a real-world surrogate marker for measuring cancer endpoints.