Biomedical Statistics and BioinformaticsMedicine and Medical Science

Ph.D. Professor Satoshi Morita

Current efforts of our department include taking advantage of huge and complex information of biomarkers for treatment effect prediction. In response to this move, we are directing our full energy to carry out new researches that synthesize biomedical-statistics and bioinformatics. The primary goal of those research activities is supporting the development of new individualized therapies. In addition, we aim to provide medical researchers with a research environment for multi-purpose, multi-faceted information analysis using a comprehensive database consisting of data from basic research to early clinical trials, manufacturing, and epidemiological research.

Lab Website

Research and Education

Our research interests are on study designs and data analysis of early to late phase clinical trials. In biomedical statistics, we are mainly focusing on developing new Bayesian approaches. In bioinformatics, we aim to propose novel statistical methods to develop effect prediction models, that is, we are performing collaborative research projects to find useful markers to predict the treatment effect. Furthermore, we are also interested in patient-reported outcome researches, e.g., quality of life assessment of cancer patients. Our department is supporting PhD and Master students who are willing to work on these research themes.

Recent Publications

  1. Morita S, Mueller P, Abe H. A semiparametric Bayesian approach to population finding with time-to-event and toxicity data in a randomized clinical trial. Biometrics. doi: 10.1111/biom.13289.
  2. Morita S, Mueller P. Bayesian population finding with biomarkers in a randomized clinical trial. Biometrics, 2017.
  3. Morita S. Yamamoto H, Sugitani Y. Biomarker-based Bayesian randomized phase II clinical trial design to identify a sensitive patient subpopulation. Stat Med 33: 4008-4018, 2014.
  4. Morita S, Thall PF, Bekele BN, Mathew P. A Bayesian hierarchical mixture model for platelet derived growth factor receptor phosphorylation to improve estimation of progression-free survival in prostate cancer. Journal of the Royal Statistical Society, Series C (Applied Statistics). 59: 19-34, 2009.
  5. Morita S, Thall PF, Müller P. Determining the effective sample size of a parametric prior. Biometrics. 64: 595-602, 2008.

Biomedical Statistics and Bioinformatics

Satoshi Morita:smorita(at)
(Office: +81-75-751-4717, Fax: +81-75-751-4767)
Associate professor:
Harue Tada:haru.ta(at)
Assistant professors:
You Hidaka yhidaka(at)
Tomohide Iwao tomohide(at)
Kentaro Ueno ueno_kentaro(at)
Aki Kubota aki_kubota(at)
Zixuan Yao yaozixuan(at)
Research Associate:
Yumiko Ibi ibi_yumiko(at)

Secretary Phone:+81-75-751-3858, FAX:+81-75-751-4732

Department of Clinical Trial Science, Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital

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