Background: The field of rheumatology is witnessing a significant shift with the incorporation of transdisciplinary approaches, essential for addressing the complex and multifaceted nature of rheumatologic conditions. This shift is further accentuated by recent advancements in artificial intelligence (AI) and data analysis tools, which have opened new avenues for research and treatment strategies. The integration of these technological advancements with traditional clinical practices is not just a forward step in rheumatology but a necessary evolution to keep pace with the rapidly changing landscape of medical research and patient care. By combining diverse fields of expertise researchers can unlock deeper insights into disease mechanisms, patient outcomes, and treatment efficacies, ultimately leading to more precise and effective patient care.

Objectives: This paper highlights the importance of integrating statistical and data science expertise in rheumatology research. It enhances discussion levels, research quality, and outcomes through a transdisciplinary approach (Klein, 2021).

Methods: The paper discusses the concept of transdisciplinarity in rheumatology, involving integrating various fields of expertise to address the complex nature of rheumatologic conditions compared to an interdisciplinary or multidisciplinary approach. It explores the use of statistical and advanced data science tools in clinical research and their role in facilitating a deeper understanding of intricate concepts such as model assumptions, validation, and reproducibility.

Results: A key finding of this study is the critical importance of involving data analysts and statisticians from the very beginning of rheumatology projects. Such early collaboration is essential for ensuring that advanced statistical models are integrated and interpreted correctly, leading to outcomes that are both meaningful and accurate. Additionally, our results underscore the effectiveness of enhancing discussion levels through hands-on, practical education. Engaging team members across all levels with interactive simulations and pedagogical methods makes complex statistical concepts more accessible and comprehensible. This method not only improves collaborative discourse but also empowers all participants to contribute more effectively to the research process. Specifically, it aids in understanding and completing the statistical plan and applying these concepts in a robust and reproducible manner.

Conclusion: Integrating statisticians and data scientists in rheumatology departments signifies a fundamental shift towards a more holistic and informed research approach. This integration is not just about adding expertise but ensuring that rheumatology research is robust, reliable, and reflective of the complex realities of these diseases. Such collaboration is vital for advancing research in this field, unraveling the complexities of rheumatologic conditions, developing effective treatments, and ultimately improving patient outcomes.

REFERENCES: [1] Klein J. T. (2021). Beyond interdisciplinarity: Boundary work, communication, and collaboration (1st ed.). Oxford University Press.

Acknowledgements: NIL.

Disclosure of Interests: None declared.