Research

Building on biostatistics, which provides methodological foundations for study design and data analysis in clinical, epidemiologic, and health sciences research, I pursue both theoretical works centered on causal inference and empirical studies in close collaboration with clinicians and epidemiologists. My research focuses on developing, refining, and implementing statistical approaches that facilitate scientific consensus among healthcare professionals, epidemiologists and policymakers. These approaches emphasize not only theoretical coherence but also a strong grounding in real-world settings.

Career

Dr. Tomohiro Shinozaki is a biostatistician and associate professor at the Interfaculty Initiative in Information Studies, the University of Tokyo. He earned his Bachelor of Health Sciences (2009), Master of Public Health (2011), and Ph.D. in Health Sciences (2016), all from the University of Tokyo.

He began his academic career in 2012 as an assistant professor in both the Interfaculty Initiative in Information Studies and the Graduate School of Medicine at the University of Tokyo, where he specialized in biostatistics and epidemiology. He later held a faculty position in the Department of Information and Computer Technology at Tokyo University of Science from 2019 to 2025, serving as lecturer and then associate professor.

His research focuses on statistical methods for clinical, epidemiologic, and health sciences research, particularly causal inference in observational studies. His methodological interests include target trial emulation, marginal structural models, and causal survival analysis. He is actively engaged in collaborative research with clinicians and epidemiologists, and has authored numerous peer-reviewed publications.

Publication lists:

• Pubmed for medical and epidemiological studies
• Google Scholar for (excerpted) methodological studies
• Researchmap including all other research achievements

Publications for Learners

Books

Causal Inference for Epidemiology and Clinical Research: Addressing Real-World Problems with Robins’ g-Methods. Asakura Publishing Co., Ltd.: Tokyo, 2026.

Biostatistics Compass: 24 Lectures for a Deeper Understanding from Study Design to Manuscript Reporting (in Japanese). Health, Labour and Welfare Statistics Association: Tokyo, 2023.

Analysis on Real-World Data: An Overview. In: Nakashima, N. (eds) Epidemiologic Research on Real-World Medical Data in Japan. SpringerBriefs for Data Scientists and Innovators, vol 2. Springer, Singapore, 2022. doi: 10.1007/978-981-19-1622-9_12

 

Review Articles (Open Access, English only)

Suzuki E, Shinozaki T, Yamamoto E. Natural effects and separable effects: insights into mediation analysis. Current Epidemiology Reports. 2025;12:20. (doi: 10.1007/s40471-025-00369-3).

Inoue K, Sakamaki K, Komukai S, Ito Y, Goto A, Shinozaki T. Confounder selection and sensitivity analyses to unmeasured confounding from epidemiological and statistical perspectives. Journal of Epidemiology. 2025;35:3–10.

Iwagami M, Ishimaru M, Takeuchi Y, Shinozaki T. When and how to split the follow-up time in the analysis of epidemiological or clinical studies with follow-ups. Journal of Epidemiology. 2025;35:161–169.

Iwagami M, Shinozaki T. Introduction to matching in case-control and cohort studies. Annals of Clinical Epidemiology. 2022;4:33–40.

Khosravi A, Nazemipour M, Shinozaki T, Mansournia MA. Population attributable fraction in textbooks: time to revise. Global Epidemiology. 2021;3:100062.

Shinozaki T, Suzuki E. Understanding marginal structural models for time-varying exposures: pitfalls and tips. Journal of Epidemiology. 2020;30:377–389.

Suzuki E, Shinozaki T, Yamamoto E. Causal diagrams: pitfalls and tips. Journal of Epidemiology. 2020;30:153–162.