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最終更新日：2026年10月16日
授業計画や教室は変更となる可能性があるため、必ずUTASで最新の情報を確認して下さい。
UTASにアクセスできない方は、担当教員または部局教務へお問い合わせ下さい。
最終更新日：2026年10月16日
授業計画や教室は変更となる可能性があるため、必ずUTASで最新の情報を確認して下さい。
UTASにアクセスできない方は、担当教員または部局教務へお問い合わせ下さい。
医学共通講義XLⅦ
STATISTICS FOR HEALTH RESEARCH.
This course provides students with a foundational understanding of statistical concepts and methods commonly used in medical and public health research. Students will learn how data are summarized, analyzed, and interpreted to address research questions and quantify uncertainly.
Emphasis is placed on practical data analysis skills, including the use of the R programming language for data management, statistical testing, regression modelling, and result presentation. The course aims to equip students with the statistical literacy needed to critically interpret scientific literature and support evidence-based decision making.
This course is designed as a complementary course to Epidemiology for Health Research and is strongly recommended to be taken together.
By the end of this course, students should be able to:
1. Explain fundamental statistical concepts, including data types, probability distributions, and sample variability.
2. Describe and apply statistical methods commonly used in epidemiology and population health research.
3. Manage, analyze, and summarize data using appropriate tables, figures, and numerical summaries.
4. Explain the principles of statistical inference, including hypothesis testing, confidence intervals, and p-values.
5. Identify the key features of the normal distribution and assess its relevance to statistical analyses.
6. Select appropriate statistical methods for the analysis of simple data sets and implement them using the R programming language.
7. Apply reproducible statistical workflows using R for data analysis and reporting.
8. Assess key assumptions and limitations of commonly used statistical models, including regression and time-to-event analyses.
9. Interpret and critically assess the output of statistical analyses in relation to the underlying research questions.
10. Present and communicate statistical findings clearly, concisely, and logically.
11. Explain the principles of power and sample size calculation and apply them to basic study design.
1. Explain fundamental statistical concepts, including data types, probability distributions, and sample variability.
2. Describe and apply statistical methods commonly used in epidemiology and population health research.
3. Manage, analyze, and summarize data using appropriate tables, figures, and numerical summaries.
4. Explain the principles of statistical inference, including hypothesis testing, confidence intervals, and p-values.
5. Identify the key features of the normal distribution and assess its relevance to statistical analyses.
6. Select appropriate statistical methods for the analysis of simple data sets and implement them using the R programming language.
7. Apply reproducible statistical workflows using R for data analysis and reporting.
8. Assess key assumptions and limitations of commonly used statistical models, including regression and time-to-event analyses.
9. Interpret and critically assess the output of statistical analyses in relation to the underlying research questions.
10. Present and communicate statistical findings clearly, concisely, and logically.
11. Explain the principles of power and sample size calculation and apply them to basic study design.
時間割／共通科目コード
コース名
教員
学期
時限
41611147
GME-ML6147L2
医学共通講義XLⅦ
橋爪 真弘
木曜3限、木曜4限
マイリストに追加
マイリストから削除
講義使用言語
英語
単位
2
実務経験のある教員による授業科目
NO
他学部履修
可
開講所属
医学系研究科
授業計画
Thursday
3rd period (13:00～14:45)
4th period (14:55～16:40)
(The course contents are subject to change)
Day 1
Data and probability distribution
Population and sample, statistical inference, hypothesis testing
Day 2
R programming
Comparing proportions: Chi-square test, exact test
Day 3
Comparing means: t-test, ANOVA
Non-parametric tests
Day 4
Stratified analysis
Correlation, linear regression
Day 5
Logistic regression
Time-to-event analysis
Day 6
Statistical modelling
Day 7
Power and sample size
授業の方法
Before the lectures
• UTokyo LMS (UTOL) access: Please ensure that you can access the course webpage on UTOL.
• R software: R script files will be distributed. Please download and install R in advance.
Participating in lectures and practical sessions
• All course information and materials will be posted on UTOL. Please check the course webpage regularly for learning materials, updates, and announcements.
• Go to the Course Documents menu and follow the step-by-step instructions for each session.
• Please download all materials for each session in advance, including lecture slides, practical questions, data files, R scripts, and solution files.
• Students are expected to view the lecture video and attempt the self-practice questions on their own computer before the in-person session.
• Before each session, you are encouraged to skim through the lecture slides and identify topics that may require additional clarification.
Preparation for practical session
• Students are advised to read the related practical questions in advance of each session.
• Please ensure that R is successfully installed and functioning on your computer (connection to the university network is not required).
• You should also confirm that your computer can read or import the datasets provided before attending the session.
• Solution files and R scripts (where applicable) will be uploaded to UTOL after each practical session.
Review
• Review sessions may be arranged following discussion with students and based on their availability. Please check UTOL for announcements.
• In addition, students may be given the opportunity to participate in group presentations based on practice questions related to statistical analyses.
成績評価方法
Assessment
• Students will be assessed at the end of the course.
• The assessment will consist of a combination of short multiple-choice questions and open-ended questions.
• Further details regarding the assessment format and schedule will be provided toward the end of the course.
履修上の注意
None.
その他
Lecturers
Masahiro Hashizume, GHP, *****
Chris Fook Sheng Ng, GHP, *****
Daisuke Yoneoka, GHP, *****
Akira Shibanuma, CGH, *****
Paul Chua, GHP, *****
Lei Yuan, GHP, *****
Aurelio Tobias, GHP, *****
