應用多變量與長期資料分析
Applied Multivariate and Longitudinal Data Analysis
| 節 | 週二 |
|---|---|
7 15:30–16:20 | 應用多變量與長期資料分析 YS405（陽明） 2 節連堂 |
8 16:30–17:20 |
* 根據陽明交大上課時間表所列
This course focuses on applied multivariate extensions of generalized linear models (GLMs) with more complex data structure such as longitudinal (repeated) measures, matching, and hierarchical samples. Within the framework of GLMs, topics include design issues, marginal model with longitudinal data using both likelihood and generalized estimating equation (GEE) approaches, random effect and multilevel models, and conditional logistic regression with matched case-control sampling scheme. Multivariate methods such as principal component analysis, factor analyses will be introduced.
Basic knowledge in statistical modelling or consent of the instructor
無備註
Class notes, references, and other teaching materials can be downloaded from e3 platform. Students are free to use any statistical software package to perform the data analysis projects. Learning guide for using SPSS (video), SAS and R codes will be provided on e3.
Class participation, Data analysis projects
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| 週次 | 主題 |
|---|---|
| 第 1 週 | Course Introduction Studies with correlated data and multivariate data |
| 第 2 週 | Introduction to the family of generalized linear models -1 -Linear, Logistic, Poisson, negative binomial, and Gamma models |
| 第 3 週 | Introduction to the family of generalized linear models -2 -Cases studies and interpretation of the model parameters with varying link functions |
| 第 4 週 | Linear regression model with correlated measures – marginal models - Likelihood approach - Generalized estimating equation (GEE) (quasi-likelihood approach) |
| 第 5 週 | Linear regression model with correlated measures – random effect models - Linear mixed model and multi-level models (likelihood approach) (1) |
| 第 6 週 | Linear mixed model and multi-level models (likelihood approach) (2) |
| 第 7 週 | Logistic regression with longitudinal data (1) - Logistic regression using GEE - Generalized linear mixed model for longitudinal binary outcome |
| 第 8 週 | Logistic regression with longitudinal data (2) |
| 第 9 週 | Conditional logistic regression - Case-control, matched case-control, and prospective studies sampling scheme - Conditional likelihood approach |
| 第 10 週 | Poisson log-linear model with GEE (1) |
| 第 11 週 | Poisson log-linear model with GEE (2) |
| 第 12 週 | Principal component analysis |
| 第 13 週 | Factor Analysis (1) |
| 第 14 週 | Factor Analysis (2) |
| 第 15 週 | Think and Questions |
| 第 16 週 | Final exam (take home) |
Diggle, Heagerty, Liang, & Zeger. Analysis of longitudinal data 2nd ed. 2002 Oxford science publications. Hosmer & Lemoshew “Applied logistic regression” 3rd ed. Liang and Zeger. Regression analysis for correlated data. Annual Review of Public Health 1993. 14:43-68. McCullagh and Nelder. Generalized linear models. 2nd ed, 1989, Chapman and Hall. Rencher & Christensen. Methods of multivariate analysis. 3rd ed. (2012)
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