Emulating Control Arms for Cancer Clinical Trials Using External Cohorts Created From Electronic Health Record‐Derived Real‐World Data

Abstract

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.

Replication of Overall Survival, Progression-Free Survival, and Overall Response in Chemotherapy Arms of Non–Small Cell Lung Cancer Trials Using Real-World Data

“…For endpoints other than OS, especially those that typically depend on radiographic criteria such as for PFS and ORR, the utility of using RWD to serve as external controls is currently unclear. Our current study builds upon the current literature by: (i) extending the work of Carrigan and colleagues for the OS endpoint to include more recent IMpower trials; (ii) replicating trial control arms with rwPFS and rwRR endpoints for the first time in these IMpower trials; and (iii) improving upon the methodology used in recent publications by incorporating patient-level data for clinical trials (7,19).…”

Augmenting control arms with real-world data for cancer trials: Hybrid control arm methods and considerations

“…Similarly, Tan et al (2021) studied 15 trials across multiple tumor types and found that the majority of trials had HR RWD ranging from 0.66 to 1.09 for the OS endpoint [ 23 ]. Such evaluations provide a sense of how much residual bias may be plausible and relevant when selecting the value of tuning parameters and assessing the overall suitability of a hybrid design for a future cancer trials with similar I/E criteria.…”

“…From an analytical perspective, of the four main steps in a hybrid controlled trial beyond typical RCT procedures (external cohort selection; baseline covariate balance; endpoint, index dates, and follow-up time definition; implementation of a borrowing method), the first three are common to fully external controls, and have been described before [ 17 , 19 , 22 , 23 ].…”

“…RWD can be applied to construct fully external comparator cohorts without randomization [ [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] ]. Alternatively, hybrid controlled trial designs that augment RCT control arms with external cohorts ( Fig.…”

“…While these methods can help to protect against unmeasured confounders and other biases, they cannot overcome fundamental differences in patient populations, patterns of care, or endpoint measurements. Any valid RWD application must carefully assess whether or not the data source is fit for purpose, and prospective validation of the data source and trial design may be necessary [ 10 , 12 , 16 , 18 , 19 , 22 , 23 , 35 , 36 ].…”