Advanced Biostatistical Analysis
( 25 Modules )

Module #1
Introduction to Advanced Biostatistics
Overview of biostatistical analysis, importance of advanced methods, and course objectives

Module #2
Review of Basic Biostatistics
Revision of fundamental concepts in biostatistics, including probability, inference, and regression

Module #3
Generalized Linear Models (GLMs)
Introduction to GLMs, including logistic regression, Poisson regression, and log-linear models

Module #4
GLMs for Binary Outcomes
In-depth analysis of logistic regression, including model building, estimation, and inference

Module #5
GLMs for Count Outcomes
In-depth analysis of Poisson regression, including model building, estimation, and inference

Module #6
GLMs for Continuous Outcomes
In-depth analysis of linear regression, including model building, estimation, and inference

Module #7
Model Diagnostics and Validation
Methods for evaluating the fit and validity of GLMs, including residual analysis and model selection

Module #8
Survival Analysis
Introduction to survival analysis, including Kaplan-Meier estimation and Cox proportional hazards model

Module #9
Cox Proportional Hazards Model
In-depth analysis of the Cox model, including model building, estimation, and inference

Module #10
Competing Risks and Multistate Models
Introduction to competing risks and multistate models, including the Fine-Gray model and Aalen-Johansen estimator

Module #11
Recurrent Event Analysis
Introduction to recurrent event analysis, including the Andersen-Gill model and frailty models

Module #12
Longitudinal Data Analysis
Introduction to longitudinal data analysis, including linear mixed effects models and generalized estimating equations

Module #13
Mixed Effects Models
In-depth analysis of linear mixed effects models, including model building, estimation, and inference

Module #14
Generalized Estimating Equations (GEEs)
In-depth analysis of GEEs, including model building, estimation, and inference

Module #15
Clustered and Correlated Data
Methods for analyzing clustered and correlated data, including clustered logistics regression and generalized linear mixed models

Module #16
Machine Learning and Biostatistics
Introduction to machine learning methods, including decision trees, random forests, and support vector machines

Module #17
Bootstrap and Resampling Methods
Introduction to bootstrap and resampling methods, including jackknife and permutation tests

Module #18
High-Dimensional Data Analysis
Methods for analyzing high-dimensional data, including principal component analysis and sparse regression

Module #19
Multiple Testing and Correction
Methods for correcting for multiple testing, including Bonferroni and false discovery rate

Module #20
Causal Inference
Introduction to causal inference, including confounding, bias, and instrumental variables

Module #21
Mediation Analysis
Methods for analyzing mediation, including causal mediation analysis and structural equation modeling

Module #22
Instrumental Variable Analysis
In-depth analysis of instrumental variable methods, including two-stage least squares and generalized method of moments

Module #23
Case-Control Studies and Matching
Methods for analyzing case-control studies, including matching and propensity scores

Module #24
Design of Experiments
Introduction to design of experiments, including randomized controlled trials and factorial designs

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Advanced Biostatistical Analysis career

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Advanced Biostatistical Analysis ( 25 Modules )

Advanced Biostatistical Analysis
( 25 Modules )