Advanced Biostatistics Syllabus
• Principles of regression
• Methods of regression
• Linear regression
• Logistic regression
• Poisson regression
• Cox proportional hazards regression
• Regression diagnostics
• Introduction to multilevel modelling
• Introduction to data imputation
• Choosing the best models
Course Structure :
1. Introduction
- Importance of Advanced Biostatistics in Medical Research
2. Principles of Regression
- Definition and Purpose of Regression Analysis
- Key Concepts: Dependent and Independent Variables, Residuals
3. Methods of Regression
- Overview of Different Regression Methods
- Use Cases and Applicability
4. Linear Regression
- Understanding Linear Relationships
- Assumptions and Interpretation
- Practical Examples in Biostatistics
5. Logistic Regression
- Modeling Binary Outcomes
- Log-Odds, Odds Ratios, and Interpretation
- Medical and Clinical Applications
6. Poisson Regression
- Handling Count Data
- Rate Ratios and Interpretation
- Epidemiological Studies
7. Cox Proportional Hazards Regression
- Survival Analysis and Hazard Functions
- Hazard Ratios and Survival Curves
- Time-to-Event Data in Biostatistics
8. Regression Diagnostics
- Assessing Model Assumptions
- Identification of Outliers and Influential Observations
- Techniques for Model Improvement
9. Introduction to Multilevel Modeling
- Hierarchical Data Structures in Biostatistics
- Mixed-Effects Models
- Handling Nested Data
10. Introduction to Data Imputation
- Dealing with Missing Data in Biostatistical Analysis
- Methods for Data Imputation
- Impact on Model Results
11. Choosing the Best Models
- Model Selection Criteria
- Cross-Validation Techniques
- Balancing Model Complexity and Fit
12. Conclusion
- Recap of Key Takeaways
- Future Trends in Advanced Biostatistics
