Increase transparency and reproducibility of real-world evidence in rare diseases through disease-specific Federated Data Networks.

Purpose: In rare diseases, real-world evidence (RWE) generation is often restricted due to small patient numbers and global geographic distribution. A federated data network (FDN) approach brings together multiple data sources harmonized for collaboration to increase the power of observational research. In this paper, we review how to increase reproducibility and transparency of RWE studies in rare diseases through disease-specific FDNs.

European Health Data & Evidence Network-learnings from building out a standardized international health data network.

Objective: Health data standardized to a common data model (CDM) simplifies and facilitates research. This study examines the factors that make standardizing observational health data to the Observational Medical Outcomes Partnership (OMOP) CDM successful.

Materials And Methods: Twenty-five data partners (DPs) from 11 countries received funding from the European Health Data Evidence Network (EHDEN) to standardize their data.

Identification of PsA phenotypes with machine learning analytics using data from two phase III clinical trials of guselkumab in a bio-naïve population of patients with PsA.

Objectives: Psoriatic arthritis (PsA) phenotypes are typically defined by their clinical components, which may not reflect patients' overlapping symptoms. This post hoc analysis aimed to identify hypothesis-free PsA phenotype clusters using machine learning to analyse data from the phase III DISCOVER-1/DISCOVER-2 clinical trials.

Methods: Pooled data from bio-naïve patients with active PsA receiving guselkumab 100 mg every 8/4 weeks were retrospectively analysed.

Haematology Outcomes Network in Europe (HONEUR)-A collaborative, interdisciplinary platform to harness the potential of real-world data in hematology.

Introduction: There remains a need to optimize treatments and improve outcomes among patients with hematologic malignancies. The timely synthesis and analysis of real-world data could play a key role.

Objectives: The Haematology Outcomes Network in Europe (HONEUR) is a federated data network (FDN) that aims to overcome the challenges of heterogenous data collected from different registries, hospitals, and other databases in different countries.

Opioid use, postoperative complications, and implant survival after unicompartmental versus total knee replacement: a population-based network study.

Background: There is uncertainty around whether to use unicompartmental knee replacement (UKR) or total knee replacement (TKR) for individuals with osteoarthritis confined to a single compartment of the knee. We aimed to emulate the design of the Total or Partial Knee Arthroplasty Trial (TOPKAT) using routinely collected data to assess whether the efficacy results reported in the trial translate into effectiveness in routine practice, and to assess comparative safety.

Methods: We did a population-based network study using data from four US and one UK health-care database, part of the Observational Health Data Sciences and Informatics network.

Nine Principles of Semantic Harmonization.

Medical data is routinely collected, stored and recorded across different institutions and in a range of different formats. Semantic harmonization is the process of collating this data into a singular consistent logical view, with many approaches to harmonizing both possible and valid. The broad scope of possibilities for undertaking semantic harmonization do lead however to the development of bespoke and ad-hoc systems; this is particularly the case when it comes to cohort data, the format of which is often specific to a cohort's area of focus.