2026 (Current Year) Faculty Courses School of Engineering Department of Industrial Engineering and Economics Graduate major in Industrial Engineering and Economics
Biostatistics
- Academic unit or major
- Graduate major in Industrial Engineering and Economics
- Instructor(s)
- Ryuji Uozumi
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Mon (W9-201) / 7-8 Thu (W9-201)
- Class
- -
- Course Code
- IEE.C435
- Number of credits
- 200
- Course offered
- 2026
- Offered quarter
- 3Q
- Syllabus updated
- Sep 16, 2026
- Language
- English
Syllabus
Course overview and goals
Biostatistics is the science that provides methodologies for how data are collected and analyzed in medical research, including basic, clinical, and epidemiological research. In this course, students will be able to understand the basic topics of biostatistics from the mathematical aspects. In addition, students will be able to understand how to prepare analysis data and how to interpret the results of statistical analysis through exercises using statistical software.
Translated with DeepL.com (free version)
Course description and aims
1) To be able to understand the characteristics of study designs used in experimental studies.
2) To be able to understand the mathematical statistics used in biostatistics and analyze data using statistical software.
3) To be able to design sample size when designing experimental studies.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
Ryuji Uozumi has practical experience in biostatistics at a pharmaceutical company and university hospitals. He will incorporate practical perspectives in this course.
Keywords
Statistical inference, confounding, randomized controlled trial, 2×2 contingency table, maximum likelihood method, logistic regression, nonparametric methods, survival analysis, Kaplan-Meier method, log-rank test, Cox proportional hazards model
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
This course will be conducted by giving a lecture to understand mathematical statistics. Computing exercises using statistical software will be included during the lecture. Students have to work on exercises using their laptop with statistical software.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Guidance |
Provide an overview of the role of biostatistics in medical research. |
| Class 2 | Overview of study designs |
Understand each study design. In addition, understand the role of randomized controlled trials. |
| Class 3 | Case study |
Provide some medical study examples and understand how to apply biostatistics. |
| Class 4 | Parameter estimate for binomial distribution |
Understand the mathematical aspects in the parameter estimate of a binomial distribution. |
| Class 5 | Categorical data analysis |
Understand how to analyze 2x2 contingency tables. |
| Class 6 | Stratified analysis for categorical data |
Understand how to analyze 2 x 2 contingency tables adjusting for confounding variables (stratified analysis, Cochran-Mantel-Haenszel test). |
| Class 7 | Statistical models for categorical data |
Understand logistic regression models for categorical data. |
| Class 8 | Statistical problems for handling survival data |
Understand statistical problems derived from survival data. |
| Class 9 | Nonparametric estimate for survival data |
Understand probability distributions used in survival analysis. In addition, understand nonparametric estimates using the Kaplan-Meier method for survival data. |
| Class 10 | Nonparametric tests for survival data |
Understand nonparametric tests (log-rank tests) for survival data. |
| Class 11 | Semiparametric models for survival data |
Understand semiparametric models (Cox proportional hazards model) for survival data. |
| Class 12 | Recent topics for p-value |
The misuse of P-values is common in experimental research. Understand the role of p-values. |
| Class 13 | Sample size calculation |
Understand how to design sample size based on statistical hypothesis testing. |
| Class 14 | Final exam |
Check the level of understanding |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class. They should do so by referring to textbooks and other course material.
Textbook(s)
Kalbfleisch JD, Prentice RL. The Statistical Analysis of Failure Time Data (Second Edition). Wiley, 2002.
Reference books, course materials, etc.
N/A
Evaluation methods and criteria
Final exam and report.
Related courses
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.A205 ： Statistics for Industrial Engineering and Economics
- IEE.C302 ： Quality Management
Prerequisites
Understand the undergraduate level in "Probability and Statistics", "Linear Algebra", and "Calculus".
Other
Bring your laptop with statistical software (Both R and R Studio).