Electronic health record (EHR)-derived real-world data (RWD) can be sourced to create external comparator cohorts to oncology clinical trials. This exploratory study assessed whether EHR-derived patient cohorts could emulate select clinical trial control arms across multiple tumor types. The impact of analytic decisions on emulation results was also evaluated. By digitizing Kaplan-Meier curves, we reconstructed published control arm results from 15 trials that supported drug approvals from January 1, 2016, to April 30, 2018. RWD cohorts were constructed using a nationwide EHR-derived de-identified database by aligning eligibility criteria and weighting to trial baseline characteristics. Trial data and RWD cohorts were compared using Kaplan-Meier and Cox proportional hazards regression models for progression-free survival (PFS) and overall survival (OS; individual cohorts) and multitumor random effects models of hazard ratios (HRs) for median endpoint correlations (across cohorts). Post hoc, the impact of specific analytic decisions on endpoints was assessed using a case study. Comparing trial data and weighted RWD cohorts, PFS results were more similar (HR range = 0.63-1.18, pooled HR = 0.84, correlation of median = 0.91) compared to OS (HR range = 0.36-1.09, pooled HR = 0.76, correlation of median = 0.85). OS HRs were more variable and trended toward worse for RWD cohorts. The post hoc case study had OS HR ranging from 0.67 (95% confidence interval (CI): 0.56-0.79) to 0.92 (95% CI: 0.78-1.09) depending on specific analytic decisions. EHR-derived RWD can emulate oncology clinical trial control arm results, although with variability. Visibility into clinical trial cohort characteristics may shape and refine analytic approaches.Contextualizing drug efficacy data from single-arm and small randomized clinical trials (RCTs) using robust external data sources and analytical methodologies is critical, especially in the regulatory approval setting for treatment of diseases that are rare or have high unmet medical need.
Objectives: Real-world data (RWD) from the clinical care of patients captured through Electronic Health Records (EHRs) is a valuable resource for research. Understanding the relationship between characteristics and outcomes of patients treated in the real world and in oncology clinical trials by producing evaluable trial-like populations from RWD can advance clinical and regulatory knowledge. Methods: RWD from the Flatiron Health EHR-derived, de-identified, longitudinal database were compared with pooled patient-level data from the three randomized controlled trials (RCTs) supporting regular approval of CDK4/6 inhibitors for patients with previously untreated hormone receptor-positive, HER- metastatic breast cancer (mBC). The RCT group included patients who received aromatase inhibitor (AI) monotherapy (RCT-control), and an experimental group (RCT-experimental) that received a cyclin dependent kinase 4/6 (CDK4/6) inhibitor + AI. The real-world control group (rwCG) of patients initiating AI monotherapy was selected using the key eligibility criteria across the RCTs. Patients from the rwCG were matched to the RCT-control and the RCT-experimental patients, respectively. Matching (1:1) was performed through the propensity score (PS) method adjusting for baseline covariates of age, race, site of disease, ECOG PS, and metastatic disease (recurrent, De novo). Multiple imputation (MI) was adopted to impute missing ECOG PS in the rwCG due to the high percentage of missingness. Progression-free survival (PFS) and overall survival (OS) were analyzed for the following groups: A) rwCG and RCT- control B) rwCG and RCT-experimental and C) RCT-control and RCT-experimental. To assess the feasibility of RCT control arm replication, we assessed whether the trial replication hazard ratio (HR) estimates from analysis B were within the published trial estimates’ 95% confidence intervals (CIs). HR and their 95% CIs were estimated using Cox proportional hazard model. Results: A total of 1292 patients were selected from the EHR-derived database to comprise the rwCG and 1827 patients were pooled across the RCTs (1106 for RCT-experimental and 721 patients for RCT-control). With MI, 520 rwCG patients were matched to the RCT-control for analysis A, and 658 rwCG patients were matched to the RCT-experimental for analysis B. The results are summarized in Table 1. The point estimate of the PFS HR comparing the rwCG to the RCT-experimental was within the 95% CIs for three RCTs. For OS, the point estimate of the HR was within the 95% CI for MONALESSA-2 but not for PALOMA-2. Conclusion: PFS and OS appeared longer in the RCT-control than in the rwCG, and the difference was more pronounced in OS. While it appears that there is greater similarity for PFS than for OS based on the results of the matched analysis of RCT- experimental vs. rwCG, evaluation of PFS results are limited by substantial differences in assessment and outcome definitions for progression between RCT-control (RECIST) and rwCG. Despite PS matching, there are apparent differences between patients treated in RCTs and routine practice, highlighting the importance of clinical setting, trial selection, study design, and use of randomization. There are still outstanding feasibility questions on the evaluation of OS and further research is required to understand factors potentially impacting the outcomes between RCTs and RWD. Table 1: Estimated Treatment Effects in PFS and OS, rwCG vs RCT-control vs RCT-experimental Citation Format: Christy Osgood, Jiaxin Fan, Xin Gao, Catherine Keane, Jonathan Bryan, James P. Roose, Erik Bloomquist, Aracelis Torres, Shrujal Baxi, Nathan Nussbaum, Fatima Rizvi, Shenghui Tang, Irene Nunes, Julia Beaver, Donna R. Rivera, Lynn Howie, Prashni Paliwal, Laleh Amiri-Kordestani. Feasibility of generating an external control comparator using RWD by matching with previously conducted RCTs: CDK4/6 Inhibitors for the treatment of Metastatic Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-13-03.
Objectives: Real-world data (RWD) from the routine care of patients with cancer captured through EHRs is a valuable resource for research. Understanding the relationship between characteristics and outcomes of patients treated in the real world and those treated in clinical trials is essential to produce evaluable trial-like populations using RWD in oncology for research and regulatory purposes. Methods: This study used: a) RWD from the Flatiron Health EHR-derived, de-identified, longitudinal database (comprising patient-level structured and unstructured data, curated via technology-enabled abstraction selected from approximately 280 US cancer clinics [~800 sites of care]) and b) patient-level data from three completed RCTs (PALOMA-2, MONALEESA-2, and MONARCH-3) including patients with previously untreated hormone receptor positive (HR+), HER2/neu negative (HER2-) mBC, then separately pooled across the trials into two treatment groups, patients who received aromatase inhibitor monotherapy (AI) or a CDK4/6 inhibitor + AI. Key eligibility criteria were similar across the RCTs and were used to select a real world external cohort (rwEC) initiating AI monotherapy on or prior to 11 Nov 2015 (end of MONARCH-3 enrollment period). Patients from the rwEC were matched separately to the control arm and experimental arm patients from the pooled RCT using propensity score method (PSM). The propensity score was estimated by a logistic regression using baseline covariates of age, race, site of disease (visceral, non-visceral), Eastern Cooperative Oncology Group Performance Status (ECOG PS) (0, 1), and metastatic disease (recurrent, new). The matching ratio was 1:1 without replacement with calipers. Covariate balance was measured by the absolute standardized mean difference (ASMD). Due to the high percentage of missing ECOG PS data, matching was repeated 100 times with imputed ECOG PS. The impact of including additional key covariates for propensity matching such as number of disease sites, bone-only disease, and prior endocrine therapy was assessed. Results: There were 1326 patients with HR+, HER2- mBC selected from the EHR-derived database who received first-line AI therapy and 1827 patients randomized in the RCTs (1106 and 721 patients for experimental and control arms, respectively). With 100 matching iterations, 563 rwEC patients on average (range, 547-572) were matched to the RCTs control arm, and 753 rwEC patients on average (range: 741-761) were matched to the RCTs experimental arm. Prior to matching, the ASMD varied widely across all prespecified baseline covariates (4.3 for the rwEC vs. RCTs control arm, 2.6 for the rwEC vs. RCTs experimental arm). After matching was performed, across all baseline covariates used in the PSM, the ASMD was reduced to be under 0.12 for the rwEC vs. RCTs control arm, and under 0.2 for the rwEC vs. RCTs experimental arm in more than 90% of the matching iterations. Analyses looking at the additional baseline covariates to the propensity matching resulted in similar ASMDs. Conclusions: EHR-derived RWD can be used to generate a cohort of patients with similar baseline characteristics to those treated on RCT. The next step in our trial emulation framework is to analyze the comparability of outcomes between these two matched cohorts. Citation Format: Laleh Amiri-Kordestani, Xin Gao, Shrujal Baxi, Erik Bloomquist, Jonathan Bryan, Lynn Howie, Catherine Keane, Paul G. Kluetz, Christy Osgood, Prashni Paliwal, Donna R. Rivera, James Roose, Julie Schneider, Harpreet Singh, Shenghui Tang, Lijun Zhang, Julia A. Beaver. Generating real-world external comparators for randomized clinical trials (RCTs) in metastatic breast cancer (mBC) using electronic health records (EHRs) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-11-05.
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