Akademik Veri Yönetim Sistemi
Journal of Evaluation in Clinical Practice, cilt.31, sa.6, 2025 (SCI-Expanded)
Aims and Objective: The field of medical statistics has experienced significant advancements driven by integrating innovative statistical methodologies. This study aims to conduct a comprehensive analysis to explore current trends, influential research areas, and future directions in medical statistics. Methods: This paper maps the evolution of statistical methods used in medical research based on 4,919 relevant publications retrieved from the Web of Science. High-frequency keywords and citation metrics were analyzed to identify research hotspots. A dual-map overlay and document co-citation analysis were performed using CiteSpace to uncover thematic clusters and track knowledge flow between disciplines. Additionally, network metrics, such as betweenness centrality and sigma, were employed to quantify the influence and novelty of publications. Results: Results identified a strong interdisciplinary exchange between medical statistics and fields such as health, nursing, molecular biology, and computer science, with clinical trials, survival analysis, and predictive modeling emerging as central themes. The influence of artificial intelligence (AI), machine learning (ML), and deep learning (DL) is growing substantially, particularly in areas such as diagnostic imaging, epidemiology, and treatment prediction, highlighting a shift towards more complex, data-driven methodologies. While traditional statistical techniques, such as survival analysis and regression, remain vital, emerging technologies are reshaping research approaches, fostering collaboration, and advancing the field's capabilities. Conclusion: Future research will likely focus on overcoming challenges related to data privacy, ethical considerations, and the need for continued biostatistics education in healthcare. This study offers a roadmap for ongoing research and highlights opportunities for future interdisciplinary collaborations to address the complexities of modern medical data analysis. This scientometrics study reveals the evolution of statistical methods used in medical research over time, evaluates frequently cited models and thematic changes, and provides implications that can enhance evidence-based decision-making processes regarding methodological choices that guide contemporary clinical practice.