For EHR-derived data to yield reliable real-world evidence, it needs to be of known and sufficiently high quality. Considering the impact of mortality data completeness on survival endpoints, we highlight the importance of data quality assessment and advocate benchmarking to the NDI.

rhIGF-I does not accelerate the recovery of renal function in ARF patients with substantial comorbidity.

Line of therapy (LoT); confidence interval (CI); first line of therapy (1L); second line of therapy (2L). Extended Data Table 2 | Validation on progression-free survival hazard ratioThe number of inclusion and exclusion criteria, the number of eligible patients and the hazard ratio of progression-free survival with confidence interval of emulated aNSCLC trials with eligibility criteria under three scenarios: original criteria of the clinical trial, fully relaxed criteria and data-driven criteria learned from results of the hazard ratio of overall survival (same as in Table 1). Article Extended Data Table 3 | Analysis in other cancersEligibility criteria for colorectal cancer (CRC), advanced melanoma and metastatic breast cancer in three scenarios. The number of inclusion and exclusion criteria, the number of eligible patients and the hazard ratio of the overall survival with confidence interval of emulated aNSCLC trials with eligibility criteria under three scenarios: original criteria of the clinical trial, fully relaxed criteria and data-driven criteria.

Oncology drug development increasingly relies on single-arm clinical trials. External controls (ECs) derived from electronic health record (EHR) databases may provide additional context. Patients from a US-based oncology EHR database were aligned with patients from randomized controlled trials (RCTs) and trial-specific eligibility criteria were applied to the EHR dataset. Overall survival (OS) in the EC-derived control arm was compared with OS in the RCT experimental arm. The primary outcome was OS, defined as time from randomization or treatment initiation (EHR) to death. Cox regression models were used to obtain effect estimates using EHR data. EC-derived hazard ratio estimates aligned closely with those from the corresponding RCT with one exception. Comparing log HRs among all RCT and EC results gave a Pearson correlation coefficient of 0.86. Properly selected control arms from contemporaneous EHR data could be used to put single-arm trials of OS in advanced non-small cell lung cancer into context.The need to bring safe and effective oncology treatment options to patients quickly is highlighted by the 21st Century Cures Act 1-3 and the Cancer Moonshot initiative. 4 Oncology research in recent years has included novel trial designs sometimes with only a single treatment arm. Precision oncology drugs increasingly receive accelerated or breakthrough regulatory approval based on these single-arm trials. An inherent feature of these designs is that a standard-of-care control arm is not included, leading to challenges in interpretation of efficacy. 3,[5][6][7][8][9]

PURPOSELarge, generalizable real-world data can enhance traditional clinical trial results. The current study evaluates reliability, clinical relevance, and large-scale feasibility for a previously documented method with which to characterize cancer progression outcomes in advanced non–small-cell lung cancer from electronic health record (EHR) data.METHODSPatients who were diagnosed with advanced non–small-cell lung cancer between January 1, 2011, and February 28, 2018, with two or more EHR-documented visits and one or more systemic therapy line initiated were identified in Flatiron Health’s longitudinal EHR-derived database. After institutional review board approval, we retrospectively characterized real-world progression (rwP) dates, with a random duplicate sample to ascertain interabstractor agreement. We calculated real-world progression-free survival, real-world time to progression, real-world time to next treatment, and overall survival (OS) using the Kaplan-Meier method (index date was the date of first-line therapy initiation), and correlations between OS and other end points were assessed at the patient level (Spearman’s ρ).RESULTSOf 30,276 eligible patients,16,606 (55%) had one or more rwP event. Of these patients, 11,366 (68%) had subsequent death, treatment discontinuation, or new treatment initiation. Correlation of real-world progression-free survival with OS was moderate to high (Spearman’s ρ, 0.76; 95% CI, 0.75 to 0.77; evaluable patients, n = 20,020), and for real-world time to progression correlation with OS was lower (Spearman’s ρ, 0.69; 95% CI, 0.68 to 0.70; evaluable patients, n = 11,902). Interabstractor agreement on rwP occurrence was 0.94 (duplicate sample, n = 1,065) and on rwP date 0.85 (95% CI, 0.81 to 0.89; evaluable patients n = 358 [patients with two independent event captures within 30 days]). Median rwP abstraction time from individual EHRs was 18.0 minutes (interquartile range, 9.7 to 34.4 minutes).CONCLUSIONWe demonstrated that rwP-based end points correlate with OS, and that rwP curation from a large, contemporary EHR data set can be reliable, clinically relevant, and feasible on a large scale.