Statistical Modeling and Applications
| Numbering Code | P-PUB01 8H138 LJ90 | Year/Term | 2022 ・ Intensive, Second semester | |
|---|---|---|---|---|
| Number of Credits | 1 | Course Type | Lecture | |
| Target Year | Professional degree students | Target Student | ||
| Language | Japanese | Day/Period | Intensive | |
| Instructor name |
SATO TOSIYA (Graduate School of Medicine Professor) TAKAHASHI FUMIAKI (Part-time Lecturer) OMORI TAKASHI (Graduate School of Medicine Program-Specific Professor) Omiya Masatomo (Graduate School of Medicine Program-Specific Assistant Professor) |
|||
| Outline and Purpose of the Course |
The use of a regression model is one of the methods of adjusting for "confounding." A typical regression model works for one continuous outcome variable. In medical research, however, there may be cases in which one is interested in the presence or absence of risk, or in which an interesting result is measured repeatedly, and a more complicated model may be required depending on the characteristics of the result. In this course, we explain regression models for various outcome variables and their methods of analysis by combining a range of examples. [Intensive lectures on October 1, 15, and 22, and 29 (Fri)] |
|||
| Course Goals |
Understand: ・The role of regression models, ・Regression models for various data characteristics and their methods of analysis, and ・Ways to perform analysis using statistical analysis software and interpret the results. |
|||
| Schedule and Contents |
Session 1, October 1, Period 3: Examples and Characteristics of Longitudial Data Session 2, October 1, Period 4: General Linear Models for Correlated Continuous Data (1) Mean and Covariance Structures Modeling Session 3, October 15, Period 3: General Linear Models for Correlated Continuous Data (2) Random effect models Session 4, October 15, Period 4: Analysis of Longitudinal Data with Missing Values Session 5, October 22, Period 3: Generalized Linear Models for Correlated Categorical Data (1) Marginal Models and Generalized Estimating Equations Session 6, October 22, Period 4: Generalized Linear Models for Correlated Categorical Data (2) Random effect models Session 7, October 29, Period 3: Review of Analysis of Longitudinal Data and Practice using SAS |
|||
| Evaluation Methods and Policy | Students will be asked to submit a report in each session. | |||
| Course Requirements |
・Students should have completed "Fundamentals of Statistical Inference" in the first semester. ・Students must have basic knowledge of linear algebra. |
|||
| Study outside of Class (preparation and review) | Review lessons taught in previous sessions. | |||
| Textbooks | Textbooks/References | Materials will be distributed in each session. | ||
| References, etc. | Dobson AJ. "一般化線形モデル入門 原著第2版" (Kyoritsu Shuppan) ISBN:978-4320018679, McCulloch C., Searle S., and Neuhaus J. 『Generalized, Linear, and Mixed Models』 (Wiley) ISBN:978-0-470-07371-1, Ikuko Funatogawa, Takashi Funatogawa "経時データ解析" (Asakura Shoten) ISBN:978-4254128550 | |||
