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

Abstract

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 ER1/HER22 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.

Real-world study of overall survival with palbociclib plus aromatase inhibitor in HR+/HER2− metastatic breast cancer

“…These rules attempt to organize real-world data to facilitate assessment across data points and patient records. The Flatiron Database has been used for multiple real-world studies of treatment patterns and clinical outcomes in breast cancer and other cancers 20 , 38 .…”

Selection on treatment in the target population of generalizabillity and transportability analyses

“…Furthermore, we argue that the statistical quantities estimated by applying popular marginalization-based or weighting-based standardization methods [8] after conditioning on treatment may not correspond to any reasonable causal estimand. To our knowledge, these issues have not received attention in the applied literature on generalizability and transportability analyses; in fact, conditioning on treatment in the target population appears to be common, both in applied and methodological work (e.g., [9][10][11][12][13][14][15]). We characterize the identification problems induced by conditioning on treatment using causal graphs and counterfactuals and we illustrate how they arise using simulated data.…”