Advanced Biostatistics
أُغلِق باب التسجيل
Welcome to Advanced Biostatistics
Welcome to Advanced Biostatistics, a course designed to navigate the complexities of modern health data—from longitudinal biomarkers and electronic health records to high-dimensional omics and time-to-event outcomes.
We begin by fundamentally re-examining the limitations of classical methods, exploring the philosophical shift between Frequentist and Bayesian paradigms, and establishing a rigorous framework for reproducible research to avoid common biases.
Our journey is meticulously structured to first master advanced regression for non-normal and correlated data through GLMs and mixed models, then delve deeply into survival analysis for both standard and complex scenarios like competing risks.
We will then equip you to handle high-dimensional data, establish causality from observational studies using cutting-edge techniques like propensity scoring and instrumental variables, and culminate in applying these integrated skills to a capstone project that emphasizes ethical, transparent, and reproducible analysis.
Advanced Biostatistics Course Content
Module 1: Advanced Foundations & Review
- Why Advanced Biostatistics?
- Frequentist vs Bayesian Paradigms
- Common Biases & Confounding
- Limitations of Classical Methods
- Reproducibility & Transparency
- Course Overview
Module 2: Advanced Regression I
- Why GLMs?
- Logistic Regression
- Multinomial & Ordinal Regression
- Poisson Regression
- Negative Binomial Regression
- Model Diagnostics
- Clinical Example & Interpretation
Module 3: Advanced Regression II
- Challenge: Repeated Measures
- Linear Mixed Models (LMM)
- Generalized Linear Mixed Models (GLMM)
- Fixed vs Random Effects
- Model Fitting & Diagnostics
- Example: HbA1c Trajectories
- Interpretation & Reporting
Module 4: Survival Analysis I
- Time-to-Event Data
- Censoring Explained
- Kaplan–Meier Estimation & Log-Rank Test
- Cox Proportional Hazards Model
- Assumptions & Diagnostics
- Clinical Example
Module 5: Survival Analysis II
- Competing Risks
- Frailty Models
- Recurrent Events
- Andersen–Gill vs PWP models
- Model Fitting & Interpretation
- Clinical Application
Module 6: Multivariate & High-Dimensional Data
- Challenges in High-Dimensional Data
- Principal Component Analysis (PCA)
- Factor Analysis / Confirmatory Factor Analysis
- Canonical Correlation Analysis (CCA)
- Partial Least Squares (PLS)
- Applications in Omics / Biomarkers
Module 7: Causal Inference
- Association vs Causation
- Propensity Score Methods
- Inverse Probability of Treatment Weighting (IPTW)
- Instrumental Variables
- Mediation & Moderation
- Clinical Example
Module 8: Ethics, Reproducibility & Capstone
- Statistical Pitfalls
- Reporting Guidelines
- Reproducible Workflows
- Capstone Project Instructions
- Expectations & Deliverables
- Example Capstone Topic
- Summary & Next Steps
Staff Memmbers :-
Baneen Alkofair
MD , BSC, MPH
Hanni Almohanna
MD, MPH, PhD
Dr. Mohammad Amahroos
CMBS, ARCPath, MSC, PhD
Course Completion Requirements
To successfully complete this course, you must meet the requirements below.
Engage with All Course Content
Mandatory- Watch 100% of the video lectures to ensure full coverage of the material.
Practice Your Knowledge
Counts Toward Score- Complete 7 sets of MCQ practice quizzes provided throughout the course.
- These quizzes are designed to reinforce learning and prepare you for the final exam.
Pass the Final Exam
Minimum Score- The final exam assesses your overall understanding of the course.
Overall Passing Rule: You must watch 100% of the videos and achieve an overall score of at least 40% from the combined practice MCQs + final exam.