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A comprehensive review of methodologies and application to use the real-world data and analytics platform TriNetX

Affiliation
Lübeck Institute of Experimental Dermatology ,University of Lübeck ,Lübeck ,Germany
Ludwig, Ralf J.;
Affiliation
Department of Cardiovascular and Metabolic Medicine ,Institute of Life Course and Medical Sciences ,University of Liverpool ,Liverpool ,United Kingdom
Anson, Matthew;
Affiliation
Institute and Comprehensive Centre for Inflammation Medicine ,University-Hospital Schleswig-Holstein ,Lübeck ,Germany
Zirpel, Henner;
Affiliation
Institute and Comprehensive Centre for Inflammation Medicine ,University-Hospital Schleswig-Holstein ,Lübeck ,Germany
Thaci, Diamant;
Affiliation
Department of Dermatology ,University Hospital Schleswig-Holstein Lübeck ,Lübeck ,Germany
Olbrich, Henning;
Affiliation
Lübeck Institute of Experimental Dermatology ,University of Lübeck ,Lübeck ,Germany
Bieber, Katja;
Affiliation
Lübeck Institute of Experimental Dermatology ,University of Lübeck ,Lübeck ,Germany
Kridin, Khalaf;
Affiliation
Institute of Medical Informatics and Statistics ,Kiel University ,Kiel ,Germany
Dempfle, Astrid;
Affiliation
Lübeck Institute of Experimental Dermatology ,University of Lübeck ,Lübeck ,Germany
Curman, Philip;
Affiliation
Centre for Musculoskeletal Research at University of Manchester ,Manchester ,United Kingdom
Zhao, Sizheng S.;
Affiliation
Department of Cardiovascular and Metabolic Medicine ,Institute of Life Course and Medical Sciences ,University of Liverpool ,Liverpool ,United Kingdom
Alam, Uazman

Randomized controlled trials (RCTs) are the gold standard for evaluating the efficacy and safety of both pharmacological and non-pharmacological interventions. However, while they are designed to control confounders and ensure internal validity, their usually stringent inclusion and exclusion criteria often limit the generalizability of findings to broader patient populations. Moreover, RCTs are resource-intensive, frequently underpowered to detect rare adverse events, and sometimes narrowly focused due to their highly controlled environments. In contrast, real-world data (RWD), typically derived from electronic health records (EHRs) and claims databases, offers a valuable counterpart for answering research questions that may be impractical to address through RCTs. Recognizing this, the US Food and Drug Administration (FDA) has increasingly relied on real-world evidence (RWE) from RWD to support regulatory decisions and post-market surveillance. Platforms like TriNetX, that leverage large-scale RWD, facilitate collaborations between academia, industry, and healthcare organizations, and constitute an in-depth tool for retrieval and analysis of RWD. TriNetX’s federated network architecture allows real-time, privacy-compliant data access, significantly enhancing the ability to conduct retrospective studies and refine clinical trial designs. With access to currently over 150 million EHRs, TriNetX has proven particularly effective in filling gaps left by RCTs, especially in the context of rare diseases, rare endpoints, and diverse patient populations. As the role of RWD in healthcare continues to expand, TriNetX stands out as a critical tool that complements traditional clinical trials, bridging the gap between controlled research environments and real-world practice. This review provides a comprehensive analysis of the methodologies and applications of the TriNetX platform, highlighting its potential contribution to advance patient care and outcomes.

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License Holder: Copyright © 2025 Ludwig, Anson, Zirpel, Thaci, Olbrich, Bieber, Kridin, Dempfle, Curman, Zhao and Alam.

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