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Use of RWD to Generate RWE in Regulatory Decision-Making

Chair:  Herbert (Herb) Pang (Genentech)

Speaker: Brian Hobbs, PhD (UT Austin)  
Title: Does Real-World Evidence Have a Role in Precision Oncology?     
Abstract: The FDA instituted a program for Accelerated Approval in 1992, which allowed for approvals on the basis of surrogate endpoints for drugs treating serious conditions that filled an unmet medical need. The Food and Drug Administration Safety Innovations Act, passed in 2012, allows accelerated approvals for appropriate drugs and indications by evaluating the effects of drugs on surrogate markers. More recently, the FDA has established three additional pathways to speed the review process for emerging therapies. These changes prompted innovations in trial design with master protocol and seamless designs. Immune checkpoint inhibitors (ICIs) have yielded promising therapies for patients experiencing refractory cancers. Trials evaluating ICIs made extensive use of phase Ib, enrolling hundreds and even more than one thousand patients into dose expansion cohorts following dose-escalation spanning multiple tumor types. This represents a departure from conventional drug development strategies, for which dose expansion cohorts were used in roughly 25% of phase trials. Moreover, in 2021 two drugs, Atezolizumab and Durvalumab, were voluntarily withdrawn from accelerated approvals for PD-L1 inhibition in advanced or metastatic bladder cancer. This presentation considers the statistical implications of expansive, uncontrolled early phase trials and discusses the potential role for real-world evidence in this setting.

Speaker: Hana Lee, PhD (FDA)
Title: FDA’s Efforts on Fostering Innovation for Evaluation of Real-World Evidence
Abstract: Since the landmark legislation of the 21st Century Cures Act in December 2016, interest in incorporating real-world data (RWD) and real-world evidence (RWE) in medical product development has increased dramatically. FDA has received and reviewed a growing number of applications of medical products utilizing RWE from the vast amount of data generated from the delivery of routine health care over the past years. Under PDUFA VI and VII, FDA has been committed to improve the quality and acceptability of RWE-based approaches in support of new intended labeling claims, including approval of new indications of approved medical products or to satisfy post-approval study requirements. This talk plans to provide an overview of FDA’s analytic efforts to foster innovation and bring efficiency into the regulatory decision making based on RWD/RWE.

Speaker: Laura Fernandes, PhD (COTA Health)
Title: Determining a Fit for Purpose Database for Use in Clinical Trials
Abstract: Single arm trials in oncology encounter some of the same challenges and biases encountered in observational data in terms of selection and confounding biases in the absence of a randomized control arm. The 21st Century Cures Act (Cures Act)  encouraged the use of Real-World Data (RWD) and Real-World Evidence (RWE) in facilitating regulatory decision-making. Since then, there has been an interest in using RWD for the purposes of an external comparator so that decisions on comparative effectiveness and safety could be made. This presentation will outline the different aspects that need to be considered when selecting a fit for purpose database in terms of relevance, reliability, completeness, and applicability to the target patient population in the context of electronic health records (EHR). Some considerations for statistical analysis to account for confounding biases will be presented. We will also look at some used case examples of EHR data submitted to the regulatory agencies and disseminate the feedback received.

Speaker: Michael Valancius, PhD (UNC)
Title: Causal Inference and Efficiency Considerations in Hybrid Trials with External Controls
Abstract: In this talk, we discuss the challenges associated with causal inference in settings where data from a randomized trial is augmented with control data from an external source to improve efficiency in estimating the average treatment effect (ATE). Through the development of a formal causal inference framework, we outline sufficient causal assumptions about the exchangeability between the internal and external controls to identify the ATE and establish the connection to a novel graphical criteria. We also discuss alternative assumptions that can offer additional protection against bias. We propose estimators, review efficiency bounds, develop an approach for efficient doubly-robust estimation even when unknown nuisance models are estimated with flexible machine learning methods, and demonstrate finite-sample performance through a simulation study. To illustrate the ideas and methods, we apply the framework to a trial investigating the effect of risdisplam on motor function in patients with spinal muscular atrophy for which there exists an external set of control patients from a historical trial.