Editor’s Note: Special Section on a Collection of Articles on Opportunities and Challenges in Utilizing Real-World Data for Clinical Trials and Medical Product Development

There have been increasing discussions on how real-world data (RWD) and real-world evidence (RWE) can play a role in health care decisions, particularly in medical product regulation, where RWD are the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources (e.g., observational studies, electronic health records, product, and disease registries, etc.), and RWE is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD (Food and Drug Administration (FDA) 2017).

Unitizing external data sources in the design and analysis of clinical trials or medical product development is not a new idea. In assessing clinical trial feasibility of a medical product, external data sources have often been used to find new hypotheses/findings, characterizing relevant patient populations and subpopulations, understanding unmet need, identifying important assumptions about the impact of potential eligibility criteria on trial feasibility. At the protocol development of the clinical trials, they have been used to estimate the expected effect size of the medical products, to calculate the sample size, and to support patient recruitment, and during the trial conduct, they might be used to change or modify the trial protocol or designs, or sometimes to stop the trial. At the end of the development of the medical product, in general, comprehensive integrated analysis of the efficacy and safety has been conducted, including other sources of information relevant to efficacy and safety of the product. Furthermore, in Japan, there is a very unique regulatory decision-making framework for evaluating off-label use of unapproved medical products, so called “Public Knowledge-Based Applications” (“Kochi Shinsei” in Japanese) (Ministry of Health and Welfare (MHLW) 1980). A sponsor is able to submit an application without conducting (additional) clinical trials, if efficacy and safety for a new indication of the medical product are recognized to be well known in the medical and pharmacological field through publications. This framework is a great practice of regulatory decision-making based on RWD/RWE.

What is happening right now? What is different from current practice? Due to the latest advanced technologies, it is much easier to gather and store huge amounts of health-related data in “real time.” It is expected that RWD/RWE can be used into medical product development/life cycle management in a more prospective way, not only for expanding indications, labeling modifications, or post-market safety monitoring, but also new medical product application, and they may have a potential to allow us to better design and conduct clinical trials and studies in the health care setting to answer questions which cannot be properly answered before in traditional clinical trials.

In traditional clinical trials, identifying enough eligible patients to conduct a well-powered trial within a reasonable period is often a practical challenge. RWD/RWE may allow for smaller, shorter trials, while ensuring scientific rigor and integrity of results. In addition, they may offer the opportunities of developing and delivering therapeutic options expeditiously to patients for their benefit. However, along with these potential benefits, the use of RWD/RWE in clinical trials and medical product development introduces additional challenges. At the core is whether one sacrifices evidence robustness or quantity, which loosely translates into how one can choose an imprecise right answer or a more precise wrong answer (Evans and Hamasaki 2022). Most of existing or newly proposed statistical methods for RWD/RWE generally require additional, even strong assumptions, which may make results from the trials less robust and difficult to interpret. This increases the likelihood that people will draw incorrect conclusions, ultimately increasing the likelihood that patients will receive potentially ineffective or unsafe treatments. Accumulated, extensive experiences are required for a practical utilization of RWD/RWE for clinical trials and medical product development.

During the last couple of years, Statistics in Biopharmaceutical Research has received many submissions that discuss statistical frameworks, methods, and information in using RWE/RWD for medical product development. I have put the recently accepted articles together into one place as the special section, featuring three white papers (Chen et al. 2023; Ho et al. 2023; Levenson et al. 2023), from the Real-World Evidence Scientific Working Group of the American Statistical Association (ASA) Biopharmaceutical Section (https://community.amstat.org/biop/workinggroups/rweswg/rweswg-home). I very much hope that this collection of the articles would help statisticians in the biopharmaceutical community understand the current issues and challenges in utilizing RWE/RWD for clinical trials and medical product development.

Funding

The author(s) reported there is no funding associated with the work featured in this article.