Real-World Evidence: A Primer

Abstract

Real-world evidence (RWE) is clinical evidence on a medical product's safety and efficacy that is generated using real-world data (RWD) resulting from routine healthcare delivery. There are several sources of RWD, including electronic health records (EHRs), registries, claims/billing data, and patient-generated data, as well as those from mobile health applications and wearable devices. Real-world data from these sources can be collected and analysed through different study designs such as prospective and retrospective cohort studies, case-control studies, and pragmatic clinical trials. Real-world evidence in the form of post-marketing surveillance has been extensively used to generate pharmacovigilance data. Of late, it has been realised that, apart from safety, RWE has additional applications in different stages of the drug approval cycle, and can be used to optimize the design of randomised controlled trials (RCTs). There has been an increasing awareness and acceptance of RWE from different stakeholders, including physicians, pharmaceutical companies, payers, regulators, and patients. Several regulatory authorities have also created frameworks and guidelines for efficient harnessing of RWE while acknowledging several challenges in RWD collection and analysis. The purpose of this review is to offer an outline of the current information on RWE, its advantages and disadvantages, as well as the associated challenges and ways to overcome them, while also throwing some light on the future of RWE.

Advancing the utilization of real‐world data and real‐world evidence in clinical pharmacology and translational research—Proceedings from the ASCPT 2023 preconference workshop

“…It was recommended that data quality matrices should be established prior to RWD analysis and sensitivity analysis should also be evaluated in RWD analysis. 40,41 RWD best practices for epidemiology have been well established and can be referenced for RWD applications in clinical pharmacology, translational research, drug development, and approval. FDA and EMA have also published guidelines on RWD/RWE for regulatory decision-making.…”

Generalizability in real-world trials

“…4 However, a key advantage of RWD/E trials is that they offer greater external validity than RCTs. [5][6][7][8] The results from RWD/E trials are potentially generalizable to broader populations. The basic requirement for such generalizability is random sampling of study participants: random sampling involves specifying a probability p ∈ (0,1) for each study participant to be included in the trial sample, 3,9 thereby avoiding selection bias.…”

Sensitivity analysis for causality in observational studies for regulatory science

“…Broadly speaking, causal interpretation must be supported by external knowledge of the data-generating process, such as study design or mechanistic knowledge about the phenomena under investigation. This external knowledge is often encoded in a causal model, and the set of models and data analysis tools concerned with the appropriateness of such causal interpretations is known as causal inference (please see our companion paper [2] on the causal roadmap for a more detailed discussion on causal models and causal inference).…”