TITLE: Leveraging Real-World Data in Medical Product Clinical Trials Design and Analysis
INSTRUCTOR: Chenguang Wang, Regeneron
MODERATOR: Naitee Ting
Abstract: Incorporating real-world data (RWD) in regulatory decision-making demands much more than “mixing” RWD with investigational clinical trial data. The RWD has to undergo appropriate analysis for deriving the right real-world evidence (RWE). Moreover, such analysis has to be integrated with the design and analysis of the investigational study for regulatory decision-making. The standard clinical trial toolbox does not offer ready solutions for incorporating RWD. Therefore, there is an unmet need for sound clinical trial design and analysis for leveraging RWE in clinical evaluations.
In this course, the instructor will cover a series of methods they have developed for leveraging real-world data in clinical trial design and analysis. Noteworthy, these work has been recognized by the FDA and received The FDA CDRH Excellence in Scientific Research Award-EXTERNAL EVIDENCE METHODS RESEARCH (GROUP) and The FDA Scientific Achievement Award-EXCELLENCE IN ANALYTICAL SCIENCE (GROUP) for extraordinary achievements in the timely development and active promotion of novel statistical methods for leveraging real-world evidence to support regulatory decision-making.
In Part I of the course, the instructor will introduce a method for proposing performance goals—numerical target values pertaining to effectiveness or safety endpoints in single-arm medical product clinical studies—by leveraging RWE. The method applies entropy balancing to address possible patient dissimilarities between the study’s target patient population and existing real-world patients and can take into account operation differences between clinical studies and real-world clinical practice.
In Part II of the course, the instructor will introduce a method that extends the Bayesian power prior approach for a single-arm study to leverage external RWD. The method uses propensity score methodology to pre-select a subset of RWD patients that are similar to those in the current study in terms of covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. The power prior approach is then applied in each stratum to obtain stratum-specific posterior distributions, which are combined to complete the Bayesian inference for the parameters of interest.
In Part III of the course, the instructor will describe an R package, psrwe, that implements a PS-integrated power prior (PSPP) method, a PS-integrated composite likelihood (PSCL) method, and a PS-integrated weighted Kaplan-Meier estimation (PSKM) method for the methods in Part II. Illustrative examples are provided to demonstrate each of the approaches.
Dr. Chenguang Wang is a Senior Director and the Head of Statistical Innovation at Regeneron. Previously, Dr. Wang was an Associate Professor with Johns Hopkins University and an FDA Mathematical Statistician at CDRH. Dr. Wang has extensive experience in clinical trial design and analysis in the regulatory setting. Dr. Wang also holds B.S. and M.S. degrees in Computer Science and has abundant experience developing user-friendly statistical software.