With the growing interest in augmenting traditional randomized trials with data from other sources, there has been tremendous progress in the methodological and regulatory areas. We provide a review of emerging themes in the literature and address strengths and weaknesses of the trending approaches. Special emphasis will be given to current approaches around the issue of bias control, robustness of inferential validity, and use of modern analytics.

The real-world data hype caused high expectations, including RCTs, might only play a minor role in future drug development. But they are rather complementary to RCT data and cannot replace them. Artificial intelligence may change drug development and time to market significantly, but will not replace past knowledge and experience. Real-World Evidence generation can be enhanced by AI and is key for public health.

Two recently developed tools, now publicly available, aim to increase fit-for-purpose use of RWE/RWD in regulatory decision-making will be presented, including proposals for reducing barriers and building trust with health authorities.

Recent guidance call for establishing data-specific operational definitions in protocols for real-world research. What are operational definitions, what do they consist of, and how can we maximize their usefulness? In this presentation, I propose a standards-based structure and method for generating clear and consistent operational definitions for study design elements that can be displayed in a human-readable format, as well as leveraged in computable ways across multiple RWD use cases.

This presentation will review the recent RWE guidance from FDA and EMA on registries, and describe some of the challenges and opportunities in using registries to support safety and effectiveness evaluations, with a focus on rare disease, pediatric, and pregnancy registries, including some practical examples. I will discuss future directions and areas for improvement.

In recent years, we have seen increasing usage of real-world data (RWD) and real-world evidence (RWE) in clinical development and life-cycle management, although many challenges remain. While there are numerous publications in RWD and RWE areas, strategic planning and tactical execution perspectives from an end-to-end process are still lacking, including the use of RWEs not only in regulatory settings but also in non-regulatory settings along with organizational infrastructure considerations. We attempt to fill this void by providing thoughts on addressing the key considerations.

Pragmatic trial designs can build the bridge between clinical trials and clinical practice, providing valuable real-world evidence, with the rigor of randomized clinical trials, to inform treatment decisions of payers, clinicians, employers, patients, and for regulatory purposes moving forward. Leveraging real-world healthcare data and linkage to claims data offers the opportunity to address novel endpoints. Operational lessons learned from conducting pragmatic trials will be shared along with case examples.

Amid growing awareness of disparities in representation, in 2022 the FDA issued a guidance document recommending the prospective development of clinical trial diversity plans to address the inclusion of representative numbers of patients from racial and ethnic subgroups. Real-world evidence can be used to better understand differences in disease prevalence, diagnosis and treatment patterns among marginalized populations. Real-world evidence can also inform trial eligibility criteria, guide site selection, and assist with planning for post-marketing data collection to ensure diversity.

Traditionally clinical development process always relied on bespoke evidence generation specific to the development phase. Rapidly growing volumes of individual patient-level data and the pace of technology enabling advanced analytics, is the pharmaceutical industry duplicating parts of clinical research? What is the value of data and evidence that already exists? This presentation will cover the many lives a patient's data can and should have.

Implementation of tokenization in clinical trials to enhance data collection and insight generation without increasing operational burden. Executed first-in-industry innovation project to link clinical trial data with real-world data (RWD) via tokenization to enable real-world effectiveness studies and long-term follow-up (LTFU) resulting in groundbreaking real-world evidence generation prior to licensure.

I will introduce our journey to advanced analytics from traditional reporting. The motivation, challenges, initial successes, and lessons learned will be shared.

This presentation will explain the strategic approach and various use cases for the application of AI throughout the Clinical Development environment at Merck.