From Bedside to Outcome: Integrating Clinical Trials and Real-World Data (RWD) to Optimize Drug Development and Patient Care

In recent years, significant therapeutic advances across multiple disease areas—including immuno-oncology, metabolic diseases such as type 2 diabetes and obesity (e.g., GLP-1 receptor agonists), and neurodegenerative conditions like Alzheimer’s disease—have resulted in a surge of clinical trial data. Concurrently, the increasing availability of high-quality real-world data (RWD) from electronic health records, claims databases, patient registries, and digital health tools presents an unprecedented opportunity to complement clinical trial evidence.

To fully realize the value of these rich data sources, there is an urgent need for integrated approaches. Specifically, bridging randomized controlled trials (RCTs) and real-world data (RWD) is essential not only to optimize drug development, but also to generate the robust, multidimensional evidence required for health technology assessment (HTA), reimbursement, and health policy decision-making. Quantitative approaches—such as population modeling and Bayesian prediction—enable synthesis of evidence from both controlled and real-world settings, directly informing the evaluation of new therapies in real-world patient populations and subsequently guiding healthcare decisions.

This Research Topic aims to highlight the practical impact of integrating clinical trial, real-world, and quantitative evidence on healthcare decisions. We welcome studies that demonstrate how such integration informs policy, reimbursement, and HTA; leverages RWD to improve trial design and regulatory submissions; applies quantitative methods to bridge trial and real-world settings; or illustrates, through case studies, how evidence generation has advanced patient care and access.

Topics of interest include, but are not limited to:

• Comparative effectiveness research using integrated RCT and/or RWD sources for HTA or reimbursement decisions

• Applications of quantitative analysis (population, Bayesian, etc.) to bridge trial and real-world settings for assessing effectiveness and improving patient access.

• Leveraging RWD to support regulatory and payer submissions as well as post-marketing evaluations

• RWD-driven management of drug-drug interactions and adverse event mitigation in clinical practice and post-market evaluations

• Case studies describing how integrated evidence generation has influenced patient access, outcomes, health policy, or reimbursement pathways

By connecting research insights “from bedside to outcome to policy,” this collection seeks to define a forward-looking paradigm for evidence generation—one that not only advances the science, but also translates into demonstrable gains in patient care, policy, and reimbursement. By focusing on integration with a clear impact on healthcare decisions, we aim to foster collaboration among clinicians, clinical pharmacologists, epidemiologists, data scientists, HTA experts, and healthcare decision-makers.