Using Electronic Health Records to Derive Control Arms for Early Phase Single‐Arm Lung Cancer Trials: Proof‐of‐Concept in Randomized Controlled Trials

Abstract

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]

Beyond Randomized Clinical Trials: Use of External Controls

“…Additionally, excluding patients treated with the investigational agent is undesirable especially in small trials where each patient's clinical experience is of considerable value. Alternatives to matching are available in which no patients treated with the investigational agent are excluded (e.g., using the propensity score for weighting or as a covariate), although trimming patients is often done with these approaches as well . In this study, matching is prespecified as the primary approach, whereas other methods are used as sensitivity analyses to assess the robustness of the results.…”

Emulation Differences vs. Biases When Calibrating Real‐World Evidence Findings Against Randomized Controlled Trials

“…Several projects, such as the RCT DUPLICATE initiative funded by the US Food and Drug Administration (FDA), National Institutes of Health (NIH), and others, have launched in the last few years with the aim of assessing whether nonrandomized database studies can in some circumstances produce conclusions on the effectiveness of medications that are similar to those provided by RCTs. 1,2 While comparison of randomized and nonrandomized findings is not new, heightened current interest is in part spurred by new initiatives at several regulatory agencies focused on assessing the role RWE can play in regulatory decision making. 3,4 The FDA defines RWE as evidence on the benefits and risks of medications derived from routinely collected healthcare data, although other data sources such as patient registries may also be used.…”

Can real-world data really replace randomised clinical trials?

“…Where the outcome is more likely to be affected by confounding by indication, thento mimic the randomisation element of an RCT and appropriately compare treatment groups -RWD studies must carefully adjust for all baseline confounders. In this regard, Carrigan et al recently report results exploring a research question more likely to be affected by confounding by indication [8]: whether control groups generated from RWD could approximate the control arms used in published RCTs in non-small cell lung cancer. In 10 of the 11 analyses conducted, hazard ratio estimates for overall survival derived from comparing RWD control arms with the intervention arm from the RCT were similar to those seen in the original RCT comparison.…”