Medidata Research Alliance
Fueled by Medidata’s platform to power smarter treatments and healthier people, the Medidata Research Alliance was created to propel our scientific research initiatives further.
The mission of the Medidata Research Alliance is to cultivate collaborative relationships throughout the scientific, technology, and medical research communities. Our primary goal is leveraging historical clinical trial data to advance medical breakthroughs and expand patients’ access to the most promising treatments. This mission is achieved through our exclusive framework that brings together academic key opinion leaders with the Medidata team’s extensive data and technology expertise.
Peer-Reviewed Research
The Medidata Research Alliance builds upon earlier work and existing partnerships with key clinicians and researchers from academia, industry leaders, and leading data scientists to conduct cutting-edge research to improve the field’s understanding of therapies.
Visit our Resource Center for a comprehensive library of research and publications from Medidata and its collaborators, with work including:
- EHA 2025: Racial and Clinical Determinants of Response in 2,304 Relapse/Refractory B-Cell Lymphomas Treated with Anti-CD19 Chimeric Antigen Receptor T-Cell in Clinical Trials
- ASH 2024: Secondary Hematologic Malignancies in Patients Following Chimeric Antigen Receptor T-Cell Therapy: Aggregated Clinical Trial Data from 1542 Patients
- PLOS ONE 2024: Automated Mapping of Electronic Data Capture Fields to SDTM
- Cancer Research Communications 2024: Hypophosphatemia Correction Reduces ICANS Incidence and Duration in Car T-Cell Therapy: A Pooled Clinical Trial Analysis
- Lung Cancer 2024: Brain Metastases in Clinical Trial Participants with KRAS-Mutated Advanced Non-Small Cell Lung Cancer Receiving Docetaxel: Pooled Data Analysis
The Medidata Research Alliance collaborates with academic physician-scientists across a variety of therapeutic areas to translate insights from data into clinical practice. We jointly conduct cutting edge research with our KOLs from a wide variety of academic institutions.
Key Elements
Learn more about the key elements of the Medidata Research Alliance.
Robust Data
Our exclusive dataset, which connects historical clinical trial data from over 36,000 cross-sponsor trials including more than 10 million patients from over 100 countries, powers our collaborative research and AI modeling. This robust data includes the original endpoints and complete covariate information, as captured and validated in the Medidata Platform. This uniquely enables in-depth research and analytics for a more thorough exploration of trends in diverse patient populations, evaluation of therapeutic efficacy, patient outcomes, and safety profiles, ultimately fueling advancements in research, treatment strategies, and medical progress.
Collaborative Framework
At the core of the Medidata Research Alliance is a proprietary, collaborative, and scalable framework committed to advancing quality clinical research. This framework unites leading academics and key opinion leaders with our highly qualified Medidata team of experts, including clinicians, biostatisticians, former regulatory officials, industry veterans, and data scientists. By acting as an extension of your team and leveraging this multi-sector expertise with data-driven research, we turn hypotheses into actionable insights, accelerating clinical innovations and expanding the boundaries of medical knowledge to fulfill our mission of powering smarter treatments and healthier people.
Established Presence
The Medidata Research Alliance explores clinically impactful hypotheses to bridge insights from data to clinical practice. Our work has focused on immunotherapies and rare diseases and serves as successful models for expanding into additional therapeutic areas.
We have co-presented with our Research Alliance partners at global conferences such as ASCO, ASH, NCCN, and EBMT-EHA on topics including:
- Managing severe treatment side effects
- Identifying optimal patient cohorts for investigational therapies (e.g., Castleman disease)
- Improving diagnostic biomarkers in rare diseases (e.g., ALS)
- Accelerating public health breakthroughs using clinical and real-world data (e.g., COVID)
