To address this unmet need, and focusing on rheumatoid arthritis and multiple sclerosis, this project will develop strategies for identifying scalable disease-progression endpoints and adverse events from EHR data by linking EHRs to registry data, using data from multiple healthcare systems for each condition. Availability of RWE for DMTs has been limited by the lack of reliable computable information on disease progression measures, which are typically captured in unstructured EHR text. This project, led by Tianxi Cai, Sc.D., and Florence Bourgeois, M.D, M.P.H., at the Harvard-MIT Center for Regulatory Science and Harvard Medical School, will develop novel approaches to generate RWE from data in electronic health records (EHRs) for assessing efficacy and safety of disease modifying treatments (DMTs) used in chronic diseases. Generating Reproducible Real-World Evidence with Multi-Source Data to Capture Unstructured Clinical Endpoints for Chronic Diseases The findings will be disseminated to researchers from industry, academia, and regulatory agencies through representative applications, software, statistical analysis plan templates, a website, and workshops and tutorial sessions. Specifically, the project team will develop a) a novel sensitivity analysis framework for the use of ECs from RWD sources to assess the robustness of results to hidden biases, and b) efficient analytic methods that selectively borrow and adjust for data discrepancies to mitigate the impact of hidden biases. External controls (ECs) from RWD may be used to construct the comparator arm in randomized controlled trials (RCTs) but concerns regarding the comparability of RWD and RCTs have limited their use in a broader context thus far. This project, led by Xiaofei Wang, Ph.D., at Duke University and Shu Yang, Ph.D., at North Carolina State University (NCSU), will develop innovative statistical methods to address hidden biases when integrating RWD to improve the efficiency and robustness of clinical trials. 2023 Grant Awards Methods to Improve Efficiency and Robustness of Clinical Trials Using Information from Real-World Data with Hidden Bias These selected projects enhance the agency’s already diverse portfolio of RWE demonstration projects that broaden our understanding on the potential use of RWD and RWE to support the approval of new drug indications or to satisfy post-approval study requirements for approved drugs. For information on other ongoing and completed demonstration projects, visit our RWD/ RWE Demonstration Projects webpage. Through this award program, the agency seeks to encourage innovative approaches to further support the use of RWE while ensuring that scientific evidence supporting marketing approvals meet FDA’s evidentiary standards. These awards follow four U01 grants awarded ( RFA-FD-20-030) in 2020. Food and Drug Administration announced four additional U01 grant awards in 2023 ( RFA-FD-23-025) to examine the use of real-world data (RWD) to generate RWE in regulatory decision-making. Whatever you call your design process - characterization, debug, validation or verification - as a digital designer you must have a state-of-the-art digital pattern generation as you push the edge of the technology envelope and race to market.As part of the agency’s real-world evidence (RWE) efforts, the U.S. Features & Benefits * Data Rate to 200 Mb/s * Data Pattern Depth 64 K/channel Speeds Characterization * Multiple Output Channels Increases Flexibility DG2020A: 12, 24, or 36 * Precise Control of Output Parameters Include: * Variable Output Delay * Variable Output Level * Tri-state Output Control * Transition Times 2 ns at 5 Vp-p * Flexible Sequence Control with Jump, Event and Nested Loops * Large Display for Easy-to-Use Data Editing * Import Pattern Data with DG-link Software Utility * Integrate into ATE Systems via GPIB/RS-232-C Interface Applications * Low Jitter for Clock Substitution * Characterize Device Timing * Simulate Missing Functions in System or Subsystem Evaluation Create Complex Data Patterns with Sophisticated Sequence, Looping, Jump on Event and Tri-state Output Control * Characterize and Verify ASIC, FPGA and DACs * Test Printer Engines or LCD Display Drivers * Construct Logic Verification Systems Utilizing Tektronix Oscilloscopes or Logic Scopes Use in Conjunction with TLA Logic Analyzer to Provide Digital Stimulus The DG2020A pattern generator provides digital designers with the high performance tools needed to evaluate digital semiconductors and logic circuits.
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