# Methods to improve efficiency and robustness of clinical trials using information from real-world data with hidden bias > **NIH FDA U01** · DUKE UNIVERSITY · 2024 · $821,870 ## Abstract PROJECT SUMMARY/ABSTRACT Randomized controlled trials (RCTs) are the gold-standard method of evaluating the safety and effi- cacy of treatments for diseases, such as cancer and neurological disorders. Due to disease hetero- geneity, disease rarity, or enrollment disparities, there are limited patients available for clinical trials, making drug development costly and time-consuming with trials failing at a high rate. The 21st Century Cures Act has called for regulatory agencies and drug developers to consider innovative clinical trial designs that bridge conventional clinical trials with real-world data (RWD) to overcome some of these limitations. External controls (ECs) from RWD have been used to construct the comparator arm in con- firmatory trials that eventually received approval from regulatory decision-makers. However, concerns regarding the validity and comparability of RWD with RCTs have limited their use in a broader context thus far. Selection bias, differences in variable definitions, and unmeasured confounding can lead to biased treatment effect estimates and incorrect inference if RWD are integrated with RCTs. The aims in this project focus on addressing hidden biases when integrating RWD to improve the efficiency of clinical trials. We will develop a novel sensitivity analysis framework for the use of external real-world controls to assess the robustness of results to hidden biases, as well as robust and efficient analysis methods that selectively borrow and adjust for data discrepancies to mitigate the impact of hidden bi- ases. We will actively engage in dissemination and translation of these new methods to researchers from industry, academic and regulatory agencies through exemplary applications, state-of-art software, statistical analysis plan template, resourceful website, and workshops and tutorial sessions. Our ulti- mate goal is to facilitate more robust use of real-world evidence in regulatory decision-making. ## Key facts - **NIH application ID:** 10913527 - **Project number:** 5U01FD007934-02 - **Recipient organization:** DUKE UNIVERSITY - **Principal Investigator:** Xiaofei Wang - **Activity code:** U01 (R01, R21, SBIR, etc.) - **Funding institute:** FDA - **Fiscal year:** 2024 - **Award amount:** $821,870 - **Award type:** 5 - **Project period:** 2023-09-01 → 2026-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10913527 ## Citation > US National Institutes of Health, RePORTER application 10913527, Methods to improve efficiency and robustness of clinical trials using information from real-world data with hidden bias (5U01FD007934-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10913527. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*