Approaches for Enhanced Extrapolation of Long-Term Survival Outcomes Using Electronic Health Records of Patients With Cancer.

OBJECTIVES: This study aimed to demonstrate enhanced survival extrapolation methods using electronic health record-derived real-world data (RWD). METHODS: The study population included patients diagnosed of ER+/HER2- metastatic breast cancer who started first-line treatment with anastrozole or letrozole between November 18, 2014, and November 18, 2015. Two patient cohorts were constructed: a clinical trial cohort from digitized MONARCH-3 clinical trial results and a RWD cohort from a deidentified electronic health record-derived database. RWD patients were weighted to trial baseline covariate distributions. Standard parametric approaches were applied to trial data and a "best-fit" model was selected. We demonstrate traditional and enhanced hybrid (pooling with weighted RWD at start, 75%, or end of trial) extrapolation approaches. RESULTS: Observed and estimated 5-year progression-free survival (PFS) rates in extrapolating the trial control arm (n = 165) were comparable across all methods. Compared with the observed 5-year mean PFS in the RWD cohort (n = 118) of 20.4 months (95% confidence interval [CI] 16.9-23.8), there was some variation among studied methods. Best-fit standard parametric model (log-normal) had 5-year mean PFS of 21.3 months (95% CI 18.2-24.9), and for the hybrid methods in order of estimate conservativeness was start of trial (20.8 months; 95% CI 18.5-23.2), 75% of trial (21.3 months; 95% CI 18.1-24.5), and end of trial (21.8 months; 95% CI 18.8-25.2). CONCLUSIONS: Our study leverages RWD to enhance long-term survival extrapolation. Future use cases should include applying patient eligibility criteria, weighting on baseline characteristics, and choice of time window to add RWD to trial data.

Using electronic health record data to identify comparator populations for comparative effectiveness research.

Electronic health records (EHRs) can define real world patient populations with high levels of clinical specificity, potentially addressing some of the shortcomings of other types of real world data (RWD) when informing decisions about the comparative effectiveness of medical technologies. An important but under-recognized concern for EHR-derived RWD, however, is that the rich clinical data permits creation of very homogenous subpopulations from the larger group of eligible patients, thereby reducing the representativeness of the cohort relative to clinical practice. In this article, we discuss the tradeoffs between choosing clinical specificity versus representativeness in population sampling for comparative effectiveness research. Using EHR-derived RWD, we provide an example in non-small cell lung cancer to illustrate the concepts, showing wide variation in outcomes among potential comparator cohorts. We close with several recommendations for selecting comparator populations from EHRs that address the balance between matching clinical guidelines and capturing practice variability in comparative effectiveness research.