| Academic Year |
2025Year |
School/Graduate School |
School of Informatics and Data Science |
| Lecture Code |
KA232001 |
Subject Classification |
Specialized Education |
| Subject Name |
生物・医療統計 |
Subject Name
（Katakana） |
セイブツ・イリョウトウケイ |
Subject Name in
English |
Biostatistics |
| Instructor |
To be announced. |
Instructor
(Katakana) |
タントウキョウインミテイ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
3rd-Year, Second Semester, Intensive |
| Days, Periods, and Classrooms |
(Int) Inte |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face |
| Lecture |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
B
:
Japanese／English |
| Course Level |
3
:
Undergraduate High-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
Medical/Epidemiological study design, Medical Statistics |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
Data Science Program
（Comprehensive Abilities）
・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability.
Intelligence Science Program
（Comprehensive Abilities）
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science. |
|---|
Class Objectives
/Class Outline |
1. To understand the basic knowledge of medical statistics
2. To learn about the medical and epidemiological study design and to select the necessary statistical methods appropriately.
3. To analysis using actual data.
|
| Class Schedule |
第1回 Simple Regression
第2回 Statistical Analysis Environment R
第3回 Likelihood & Multiple Regression
第4回 Generalized linear model
第5回 Logistic regression
第6回 Longitudinal data analysis
第7回 Random intercept model
第8回 Intra-subject dependence
第9回 Data analysis with R
第10回 Survival distributions
第11回 Estimating survival function
第12回 Hazard function
第13回 Mixture and frailty model
第14回 Multivariate survival analysis
第15回 Surrogate endpoints (OS, DFS, PFS) |
Text/Reference
Books,etc. |
The followings are useful for further studies. However, you do not need to review or buy them to complete the class.
[1] A Handbook of Statistical Analyses using R, Third Edition Modern Medical Statistics: A Practical Guide
[2] Balan, T. A., & Putter, H. (2020). A tutorial on frailty models. Statistical Methods in Medical Research, 29(11), 3424-3454.
[3] Taketomi, N., et al. (2022). Parametric distributions for survival and reliability analyses, a review and historical sketch. Mathematics, 10(20), 3907.
[4] Emura T, Matsui S, Rondeau V (2019), Survival Analysis with Correlated Endpoints, Joint Frailty-Copula Models, JSS Research Series in Statistics, Springer |
PC or AV used in
Class,etc. |
Handouts |
| (More Details) |
|
| Learning techniques to be incorporated |
Quizzes/ Quiz format, Post-class Report |
Suggestions on
Preparation and
Review |
Review statistics courses, including regression analysis and hypothesis test. Review the R programming. |
| Requirements |
|
| Grading Method |
The score of quizes and tests in class |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |