Description of the Course

This course aims to teach students to use advanced statistical modeling techniques and to interpret the results in the context of the research objectives. The training will focus on statistical models for Gaussian and non-Gaussian data in the context of health and related fields. These models include General Linear Models, Generalized Linear models (GLM), Linear Mixed Effect models (LMM), Generalized Linear Mixed Effect Models (GLMM), Multivariate Methods (PCA, FA, & SEM), semi parametric regression, nonlinear models, Survival data analysis methods (non-parametric methods, semi-parametric methods and parametric methods), Difference in Difference (DID), and Propensity Score matching (PSP). In addition, brief introduction will be given on Stata 16 for data management

Content

Chapter 1: Research Data Management

  • General Introduction
  • Introduction to STATA

Chapter 2: Introduction to basic statistical models

  • General Linear regression

  • Generalized Linear Models

    • Multinomial Logistic regression
    • poisson regression, and their extensions

Chapter 3: Hierarchical/Multilevel/Longitudinal/Correlated Models

  • Model for longitudinal Gaussian data (Linear mixed effect model)
  • Model for correlated Non-Gaussian data (GLMM, GEE)
    • Missing mechanism
    • Missing data management

Chapter 4: Survival Data Analysis

  • Introduction to survival analysis
  • Nonparametric procedures
  • Semiparametric modeling survival data
  • Parametric survival models
  • Computing risk models
  • Frailty models

Chapter 5: Multivariate Methods

  • Principal Components
  • Factor Analysis
  • Structural equation modeling

Chapter 6: Other types of Models

  • Difference-in-differences method (DID)
  • Propensity-Score Matching (PSM)
  • Time-series Analysis

Chapter 7: Nonparametric methods

Tentative Schedule (December 21-28, 2022)

Topics Date Responsible
Chapter 1: Research Data Management Day 1 Tadesse A Malede M
- General Introduction
- Introduction to STATA
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Chapter 2:Basic statistical models Day 2-3 Tadesse A Malede M
- General Linear regression
- Generalized Linear Models
- Multinomial Logistic Regression
- Poisson Regression
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Chapter 3: Models for Correlated Data Day 4-5 Tadesse A Malede M
- Model for longitudinal Gaussian data
- Model for correlated Non-Gaussian data
- Generalized Estimating Equation (GEE)
- Generalized Linear Mixed Model (LMM)
- Missing mechanism
- Missing data handling
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Chapter 4: Survival Data Analysis Day 6 Tadesse A Malede M
- Introduction to survival analysis
- Nonparametric procedures
- Semiparametric modeling survival data
- Parametric survival models
- Computing risk models
- Frailty models
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Chapter 5: Multivariate Methods Day 7 Tadesse A Malede M
- Principal Components
- Factor Analysis
- Structural equation modelling
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Chapter 6: Other Models Day 8 Tadesse A Malede M
-Difference-in-differences method (DID)
-Propensity-Score Matching (PSM)
-Time-series Analysis
-------- ------- -----------------
Chapter 7: Nonparametric methods Day 8 Tadesse A Malede M
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References

  1. David Collett. Modeling survival data in medical research
  2. David Collett. Binary data Modeling
  3. Elisa T. Lee, John Wenyuwang. Statistical Methods for Survival Data Analysis
  4. John Kloke: Joseph W. McKean: Nonparametric Statistical Methods Using R
  5. Michael H. Kutner, Christopher J. Nachtsheim, John Neter and William Li: Applied Linear Statistical Models
  6. Geert Molenberghs Geert Verbeke: Models for Discrete Longitudinal Data