Novel statistical approaches and applications in leveraging real-world data in regulatory clinical studies
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Novel statistical approaches and applications in leveraging real‑world data in regulatory clinical studies Heng Li1 · Wei‑Chen Chen1 · Nelson Lu1 · Chenguang Wang2 · Ram Tiwari1 · Yunling Xu1 · Lilly Q. Yue1 Received: 16 April 2020 / Revised: 2 August 2020 / Accepted: 22 August 2020 © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2020
Abstract In medical product development, there has been a growing interest in utilizing real-world data which have become abundant owing to advances in biomedical science, information technology and engineering. High-quality real-world data may be utilized to generate real-world evidence for regulatory or healthcare decision-making. We discuss propensity score-based approaches for leveraging patients from a real-world data source to construct a control group for a non-randomized comparative study or to augment a single-arm or randomized prospective investigational clinical study. The proposed propensity score-based approaches leverage real-world patients that are similar to those prospectively enrolled into the investigational clinical study in terms of baseline characteristics. Either frequentist or Bayesian inference can then be applied for outcome data analysis, with the option of downweighting information from the real-world data source. Examples based on pre-market regulatory review experience are provided to illustrate the implementation of the proposed approaches. Keywords Real-world data · Real-world evidence · Propensity scores · Constructing external control group · Augmenting patient cohort
1 Introduction In recent years, there is a growing interest in leveraging real-world data (RWD) to generate real-world evidence (RWE), which is then used to inform decision-making in medical product development or healthcare. RWD refer to data relating to patient health status and/or the delivery of healthcare routinely collected from a variety of sources, such as electronic health records (EHRs), insurance claims and billing data, patient registries * Lilly Q. Yue [email protected] 1
Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA
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Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA
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Health Services and Outcomes Research Methodology
(product or disease) and lab test databases. When sound study design and proper analytical methods are applied to RWD, clinical evidence can be produced regarding the usage and potential benefits and risks of a medical product, and this is what we call RWE. Studies involving RWD typically have features that are distinct from traditional clinical studies, and often the standard methodological toolbox for the latter does not offer ready solutions for the former. In such cases, tailored statistical innovations for study design and outcome analysis becom
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