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Current Trends and Future Directions of Statistical Methods in Medical Research: A Scientometric Analysis
Fatma Yardibi1, Chaomei Chen2, Cagdas Hakan Aladag3,4
1Department of New Media and Communication, Faculty of Communication, Akdeniz University, Antalya, Turkey.
Journal of Evaluation in Clinical Practice
|September 8, 2025
Summary
Medical statistics is evolving with AI, machine learning, and deep learning driving new research in areas like clinical trials and predictive modeling. Future work will address data privacy and biostatistics education.
Area of Science:
- Medical statistics
- Biostatistics
- Health Informatics
Background:
- Medical statistics has advanced significantly through innovative methodologies.
- Understanding current trends and influential research is crucial for future development.
Purpose of the Study:
- To comprehensively analyze current trends, influential research areas, and future directions in medical statistics.
- To map the evolution of statistical methods in medical research.
Main Methods:
- Scientometric analysis of 4,919 publications from Web of Science.
- Dual-map overlay and document co-citation analysis using CiteSpace.
- Network metrics (betweenness centrality, sigma) to quantify influence and novelty.
Main Results:
- Strong interdisciplinary exchange identified with health, nursing, molecular biology, and computer science.
- Clinical trials, survival analysis, and predictive modeling are central themes.
- Growing influence of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in diagnostics, epidemiology, and treatment prediction.
Conclusions:
- Future research will focus on data privacy, ethical considerations, and biostatistics education.
- The study provides a roadmap for research and highlights opportunities for interdisciplinary collaboration.
- Scientometrics reveals evolution, influential models, and thematic changes to enhance evidence-based decision-making in clinical practice.
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