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Medical Statistics Lab
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A lab that devises statistical methods for use in medical research.
AboutAbout the lab
Contributing to medical research through statistics and mathematical science
The Medical Statistics Lab studies the development and application of statistical and mathematical methods useful for medical research.
Its methods cover observational studies using existing databases, clinical trials, research synthesis that combines the results of multiple studies, and analysis of cancer registry data.
It also works on methods for survival analysis and for meta-analysis, which evaluates the results of multiple studies together.
In the master's program, students learn the fundamentals of medical statistics and programming skills. In the doctoral program, they aim to acquire the ability to develop new methods.
Key terms
- Confounder
- Another factor related to both the factor under study and the outcome, which creates an apparent association between them
- Propensity score
- A value expressing how likely a patient is to receive a given treatment, calculated from patient characteristics (covariates). It is used to adjust for bias in observational studies
- Survival analysis
- Statistical methods for analyzing the time until an event occurs
- Meta-analysis
- A method of statistically combining and analyzing the results of multiple studies
ResearchResearch themes
The lab develops statistical and mathematical methods useful for medical research and advances their application. Its research covers observational studies, clinical trials, the synthesis of results from multiple studies, and analysis of cancer registry data.
Adjusting for confounders in observational studies
Observational studies that use existing databases require adjustment for the effects of confounders. The lab studies multiply robust estimation methods, which allow multiple models for the propensity score.
Sensitivity analysis for unmeasured confounders
The lab studies sensitivity analysis methods that estimate how much an unmeasured confounder could affect analysis results. It is working on methods that give upper and lower bounds on the average causal effect under minimal assumptions.
Survival analysis in clinical trials
The lab studies analysis methods and trial designs for randomized clinical trials with survival time as the endpoint. It proposes methods that handle tests incorporating covariates, recurrent events, and restricted mean survival time.
*Illustrative imageBlinded sample size re-estimation
The lab studies sample size re-estimation, which revisits the target number of participants using interim data from a clinical trial. It covers methods performed under blinding, without revealing treatment assignments, and proposes methods that work even when the sample size is extremely small.
MembersFaculty
The members of the Medical Statistics Lab consist of faculty members, doctoral and master's students, research students, and secretaries.As of 2026
- ProfessorSatoshi Hattori
- Assistant ProfessorMasaki Yoshioka
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Würfel, gemischt” Dietmar Rabich / CC BY-SA 4.0 (cropped)
- Blinded sample size re-estimation: “Clinical Trial Participant Receives Injection ( )” NIAID / CC BY 2.0 (cropped)
- Related lab (Hospital Pharmacy Lab): “US Navy 040106-N-7408M-001 Hospital Corpsman 2nd Class Sonia R. Fikes from Bogalusa, La., dispenses medication for a prescription in the Pharmacy aboard USS Enterprise (CVN 65)” U.S. Navy photo by Photographer's Mate Airman Jusitn McGarry. / Public domain (cropped)
- Related lab (Hematology and Oncology Lab): “Blood cells under the microscope 01” Korinna / CC BY 4.0 (cropped)
- Related lab (Urology Lab): “Workingelement” Ramonduran / CC BY 3.0 (cropped)
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