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Review
. 2025 Mar 10:16:1516126.
doi: 10.3389/fphar.2025.1516126. eCollection 2025.

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

Ralf J Ludwig  1   2   3 , Matthew Anson  4   5 , Henner Zirpel  3 , Diamant Thaci  3 , Henning Olbrich  2 , Katja Bieber  1 , Khalaf Kridin  1   6   7 , Astrid Dempfle  8 , Philip Curman  1   9   10   11 , Sizheng S Zhao  12 , Uazman Alam  4   5   13
Affiliations
Review

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

Ralf J Ludwig et al. Front Pharmacol. .

Abstract

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.

Keywords: Kaplan–Meier estimator; TriNetX; cohort study; drug discovery; real-world data.

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Conflict of interest statement

HZ has received financial support for attending meetings and/or travel from Pfizer, UCB Pharma, Almirall, Janssen, and TriNetX. HO has received financial support to attend meetings from TriNetX. RL has received honoraria for speaking or consulting or has obtained research grants from Novartis, Lilly, Bayer, Dompe, Synthon, Argen-X, Pharmaxis, CSL, TriNetX and Monasterium Laboratories during the last 3 years. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

FIGURE 1
FIGURE 1
Structure of and number of publications with the TriNetX platform. (A) Electronic health records (EHRs) encompassing longitudinal data on demographics, diagnoses, medication, laboratory results, procedures and (limited) genetics (depicted as DNA) are provided by each of the TriNetX cooperating healthcare organizations (HCOs). Data from the HCOs’ EHR system are pseudonymized and then transferred to a TriNetX server that is located behind the HCO firewall. Analysis by an end-user accessing the TriNetX platform only allows to analyze aggregated data from several HCOs. (B) Number of publications using or referring to the TriNetX platform. In 2018, two manuscripts using TriNetX were published. This number increased to three in 2019, and more rapidly thereafter, reaching close to 300 from January to June 2024 (depicted in grey). Based on this, we assume close to 700 publications using the TriNetX platform in 2024, with another steep increased in the subsequent years.
FIGURE 2
FIGURE 2
Workflow of the analytical steps using TrinetX.
FIGURE 3
FIGURE 3
Definition of index event, baseline characteristics and outcomes. On the TriNetX platform, the index event that is defined by a specific occurrence or point in time to define the starting point for follow-up is defined by the selected criteria for each cohort. Baseline characteristics of the cohorts occurring at any time (up to 20 years) prior to the index event can be used for propensity-score matching or for an in-depth description of the cohorts. Outcomes following the index event, again for up to 20 years after the index event, are by default considered 1 day after the index event.
FIGURE 4
FIGURE 4
Schematic overview of the research enabled by real-world data (RWD) exemplified by TriNetX. The diagram highlights key areas of application, including (i) alternatives for randomized controlled clinical trials, (ii) drug-repurposing, (iii) interlinking basic and translational research, (iv) machine learning, (v) identification of risks in populations, (vi) providing data for healthcare policy and decision making, (vii) identification of racial disparities, and (viii) evaluation of drug safety and efficacy.
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