Real World Evidence Framework in Clinical Development

Real-world data (RWD) and real-world evidence (RWE) have historically played an important role in drug development and patient access. In PDUFA VI that has been in effect since October 2017, 'exploring the use of real world evidence for use in regulatory decision-making' is specified to enhance regulatory science and expedite drug development, for both effectiveness and safety evaluations. Since then, there is a surge of innovative trial designs and methodologies that incorporate RWE in clinical setting. The COVID-19 pandemic also sheds a unique light on alternative data sources usage in understanding the virus and disease, including off-label use of drugs in COVID-19 treatment. In the last quarter of 2021, the FDA published a series of four guidance documents on RWD utilization standards and strategies in fulfillment of the mandate under section 505F of the FD&C Act. This short course will start by defining RWD and RWE and then present the FDA framework for using RWD in clinical development. We will take a closer look into the FDA guidance documents. Each guidance has its specific purpose, ranging from electronic health records (EHRs) and medical claims ('Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products'), to registries ('Real-World Data: Assessing Registries to Support Regulatory Decision-Making for Drug and Biological Products Guidance for Industry') in support of submissions for drug and biologic approval, including data standards specific to RWD ('Data Standards for Drug and Biological Product Submissions Containing Real-World Data') and other considerations ('Considerations for the Use of Real-World Data and Real-World Evidence To Support Regulatory Decision-Making for Drug and Biological Products') in regulatory decision-making. We will also define the process of evaluating a fit for purpose database that is relevant and applicable to the research question. The next section will focus on the different types of systemic biases: selection, information and confounding, and the methods that can be used to mitigate their effects. Different types of external controls, e.g., historical control, external control, synthetic control, pragmatic, and hybrid control, will be presented along with their pros and cons. Special focus will be given on the different biases that come in play when using these external controls in a clinical trial and the ways to mitigate them. Finally clinical trial designs and Bayesian statistical methodologies that leverage external clinical trial data will be introduced. Methods to borrow patient-level and study-level data into a clinical trial design will be discussed. Special considerations in the incorporation of RWD into the various stages of clinical trials, Phase 1 through Phase 4, will also be presented. Multiple case examples will be used throughout the course to illustrate the trial designs and statistical methodologies.