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Uozumi Lab
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A biostatistics lab that contributes to the advancement of medicine through the power of statistics.
AboutAbout the lab
Contributing to the advancement of medicine through biostatistics
The Uozumi Lab studies biostatistics, the statistics of medical research.
Its interests include the design and analysis of clinical trials, survival analysis, and causal inference.
The lab works on both methodological research that develops new statistical methods and applied research that uses statistics in actual studies.
Associate Professor Uozumi has extensive experience advising and supporting medical researchers who come with statistical questions.
Key terms
- Biostatistics
- The discipline of analyzing data collected in medical and biological research with statistical methods to draw reliable conclusions
- Clinical trial
- A trial conducted to confirm whether a new drug or treatment is effective and safe in humans
- Survival analysis
- A statistical method for analyzing the time until an event, such as disease recurrence, occurs
- Causal inference
- Methods for determining, from data, how much a given cause influenced an outcome
ResearchResearch themes
The lab aims to contribute to the advancement of medicine through biostatistics. Its research covers the design and analysis of clinical trials, survival analysis, causal inference, and more.
*Illustrative imageAccurately Measuring the Effects of New Treatments
In cancer immunotherapy, effects appear with a delay, so ordinary survival analysis can misjudge how well a treatment works. The lab compares which analysis methods are accurate in clinical trials, and also studies methods for estimating measures of diagnostic accuracy, which evaluate how well a test distinguishes diseases.
*Illustrative imageExploring Causation from Observational Data
For rare diseases and similar cases, trials that assign treatments at random can be difficult to conduct. The lab works on methods that statistically adjust for differences in background between people who received a treatment and those who did not, in order to correctly estimate treatment effects from observational data.
MembersFaculty
- Associate ProfessorRyuji Uozumi
VoicesStudent voices
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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: “Tablets pills medicine medical waste” Pöllö / CC BY 3.0 (cropped)
- Accurately Measuring the Effects of New Treatments: “Kaplan-Meyer plot” Volkdentoklin / CC BY-SA 4.0 (cropped)
- Exploring Causation from Observational Data: “Empty Beds in Ward of Hospital, Minneapolis Health Department” City of Minneapolis Archives / CC BY 2.0 (cropped)
- Related lab (市瀬研究室): “Bookshelves of red-covered journals” PeaceSeekers / CC BY 4.0 (cropped)
- Related lab (小笠原研究室): “Former Kaichi School 2009” Wiiii / CC BY-SA 3.0 (cropped)
- Related lab (永田研究室): “Elegant boardroom (Unsplash)” Benjamin Child / CC0 (cropped)
- Related lab (小林研究室): “Aerial photograph of the Port of Miami Container Port” James R. Tourtellotte / Public domain (cropped)
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