Kyoto University School of Public Health


face1Tosiya Sato, Ph.D., Professor


Graduate students interested in the methods of biostatistics and the conduct of epidemiologic research and clinical trials are welcomed. This Department offers programs of study leading to a master’s degree and a doctoral degree. The Master of Public Health (MPH) degree program is designed to provide basic training in biostatistics as applied to a broad spectrum of health-related problems. The doctoral degree program is designed to provide advanced, research oriented training in methods of biostatistics.

Research and Education

Major research fields are biostatistics methodology and conducts of epidemiologic studies and clinical trials. In methodological work, new epidemiologic study designs which are efficient and convenient are investigated for providing new sampling and analysis methods. Causal inference is the most challenging methodological research field. Estimation of causal parameters in experimental and observational studies is developing. Several collaborative studies in epidemiology and clinical trials are ongoing. Department of Biostatistics offers “Fundamentals of Biostatistics”, “Introduction to Statistical Computing and Data Management”, “Statistical Methods in Observational Studies”, “Intermediate Biostatistics”, and “Health Data Processing Laboratory”. We give introduction to concepts of randomization, causal effects, and confounding, designs of clinical trials and observational studies. Topics include randomization, experimentation, measurement, probability, confidence intervals, and tests of hypotheses.


International Biometric Conference 2012

Recent Publications

  1. Takada M, Sozu T, Sato T. Practical approaches for design and analysis of clinical trials of infertility treatments: Crossover designs and the Mantel-Haenszel method are recommended. Pharmaceutical Statistics 2015; 14: 198-204.
  2. Oba K, Sato T, Ogihara T, Saruta T, Nakao K. How to use marginal structural models in randomized trials to estimate the natural direct and indirect effects of therapies mediated by causal intermediates. Clinical Trials 2011; 8: 277-287.
  3. Noma H, Matsui S, Omori T, Sato T. Bayesian ranking and selection methods using hierarchical mixture models in microarray studies. Biostatistics 2010; 11: 281-289.
  4. Chiba Y, Sato T, Greenland S. Bounds on potential risks and causal risk differences under assumptions of confounding parameters. Statistics in Medicine 2007; 26: 5125-5135.
  5. Cai Z, Kuroki M, Sato T. Non-parametric bounds on treatment effects with non-compliance by covariate adjustment. Statistics in Medicine 2007; 26: 3188-3204.
  6. Sato T, Matsuyama Y. Marginal structural models as a tool for standardization. Epidemiology 2003; 14: 680-686.


Professor Tosiya Sato, Ph.D.
Assistant Professor Naohiro Yonemoto, MPH
Secretary Hiroko Kobayashi