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Utilizing Real-World Data (RWD) to Inform a Confirmatory Basket Trial (CBT) Design: Studying Use of Rituximab in Autoimmune Diseases (303675)

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*Robert Allen Beckman, Dept of Biostatistics, Bioinformatics, & Biomathematics, Georgetown University Medical Center 
Daphne Guinn, Program for Regulatory Science & Medicine, Georgetown University 
Linchen He, Dept of Biostatistics, Bioinformatics, & Biomathematics, Georgetown University Medical Center 
Valeriy Korostyshevskiy, Dept of Biostatistics, Bioinformatics, & Biomathematics, Georgetown University Medical Center 

Keywords: Real World Evidence, Confirmatory Basket Trials, Simulations, Auto-immune DIseases

BACKGROUND: We have previously developed a randomized confirmatory basket trial design that controls type I error by indication, yet improves development efficiency, and simulation methods to optimize design parameters based on projected outcomes. In this study, we determine how the inclusion of real-world data (RWD), collected from the electronic health record (EHR), can influence the selection of indications, trial endpoints, and design parameters for a randomized confirmatory basket trial aimed at simultaneously assessing rituximab use in combination with control corticosteroid therapy in multiple autoimmune indications sharing a common pathogenesis. METHODS: Our basket trial was informed by information about off-label use. We performed two types of simulations. First, systematic literature review alone was used for indication selection and effect size estimation. Second, for the simulations including both RWD and literature data, the EHR from Georgetown/Medstar Health was retrospectively analyzed to identify rituximab off-label use. Patient inclusion/exclusion criteria for each disease were created, clinical data extracted, and RWD data sets constructed. Simulations evaluated study metrics, such as power, bias, type I error and benefit-cost ratio. RESULTS: We first selected four indications without RWD. From RWD, we found 657 patients treated with rituximab off-label. Three indications with sufficient RWD and high success likelihood were pursued. Clinical endpoints derived from structured data, such as laboratory values, were easily accessible, but the most relevant information was often found in unstructured physician notes. We will present a comparison of results from simulations informed by both RWD and literature, compared to literature alone. SUMMARY: In this study, RWD informed indication selection and optimization of design parameters for a randomized confirmatory basket trial of rituximab therapy in rare autoimmune diseases.