Biostatistics and Epidemiology

Aims & Scope

Biostatistics & Epidemiology is the official publication of the International Biometric Society – Chinese Region.

The journal provides a platform for the dissemination of new statistical methods and the promotion of good analytical practices in biomedical investigation and epidemiology.

The journal has four main sections: -Method and Theory – Papers should contain new statistical methods and theory that are motivated by real clinical applications. -Applications – Papers should provide comprehensive examples from biomedical sciences and epidemiology and clearly articulate the appropriateness of the chosen statistical methods in addressing significant clinical or research questions. -Software and Computing – Papers should highlight the utilization of software/computing algorithms in statistical methods related to biomedical science and epidemiology. -Tutorials – Papers should be educational pieces or critical reviews of statistical methods in important and emerging areas of biomedical science and epidemiology.

Case studies of substantial scientific and methodological importance are also welcomed.

Proposals for special issues are encouraged, and should be discussed with the Editor-in-Chief.

View Aims & Scope

---

Metrics & Ranking

SJR (SCImago Journal Rank)

Year	Value
2024	0.187

Quartile

Year	Value
2024	Q4

Journal Rank

Year	Value
2024	22016

Journal Citation Indicator

Year	Value
2024	20

---

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Embase

---

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Medicine, designed to support cutting-edge academic discovery.

---

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• A tutorial on kernel density estimation and recent advances

  Citation: 536

  Authors: Yen-Chi

• Quasi-experimental design

  Citation: 126

  Authors: Matthew L.

• Statistical modeling methods: challenges and strategies

  Citation: 49

  Authors: Steven S., Richard M., T. Michael

• Essential concepts of causal inference: a remarkable history and an intriguing future

  Citation: 26

  Authors: Donald B.

• Multi-state models and missing covariate data: expectation–maximization algorithm for likelihood estimation

  Citation: 14

  Authors: Wenjie, Lijie, Erin L., David W., Hiroko H., Richard J.

• Clinical data quality: a data life cycle perspective

  Citation: 13

  Authors: Chunhua

• Regression trees for longitudinal data with baseline covariates

  Citation: 13

  Authors: Madan Gopal, Jaroslaw

• Classification of ADNI PET images via regularized 3D functional data analysis

  Citation: 11

  Authors: Xuejing, Bin, Ji, Robert, Kirk

• Application of concordance probability estimate to predict conversion from mild cognitive impairment to Alzheimer's disease

  Citation: 10

  Authors: Xiaoxia, Yilong, Yongzhao

• Challenges and strategies in analysis of missing data

  Citation: 10

  Authors: Xiao-Hua

---