Advanced Biostatistics for Medical Research: Biostatistical Modeling for Health Data
( 24 Modules )

Module #1
Introduction to Advanced Biostatistics
Overview of biostatistical modeling in medical research, importance of advanced biostatistical methods, and course objectives

Module #2
Review of Linear Regression
Refresher on linear regression, assumptions, and inference, with a focus on applications in health data

Module #3
Introduction to Generalized Linear Models (GLMs)
Theory and application of GLMs, including binary and count outcomes

Module #4
Logistic Regression for Binary Outcomes
In-depth coverage of logistic regression, including model building, interpretation, and model evaluation

Module #5
Poisson Regression for Count Outcomes
Theory and application of Poisson regression, including model interpretation and evaluation

Module #6
Introduction to Survival Analysis
Basic concepts of survival analysis, including types of survival data, censoring, and Kaplan-Meier estimates

Module #7
Cox Proportional Hazards Model
Theory and application of the Cox model, including model building, interpretation, and model evaluation

Module #8
Extensions to the Cox Model
Time-dependent covariates, non-proportional hazards, and frailty models

Module #9
Introduction to Longitudinal Data Analysis
Overview of longitudinal data, types of longitudinal studies, and research questions

Module #10
Linear Mixed Effects Models
Theory and application of linear mixed effects models for continuous outcomes

Module #11
Generalized Linear Mixed Models (GLMMs)
Extension of GLMMs to binary and count outcomes

Module #12
Conditional Logistic Regression for Matched Case-Control Studies
Theory and application of conditional logistic regression

Module #13
Introduction to Machine Learning in Biostatistics
Overview of machine learning concepts, including supervised and unsupervised learning

Module #14
Decision Trees and Random Forests
Theory and application of decision trees and random forests for classification and regression

Module #15
Regression Trees and Model-Based Recursive Partitioning
Theory and application of regression trees and model-based recursive partitioning

Module #16
Introduction to Bayesian Methods in Biostatistics
Overview of Bayesian inference, including prior distributions and posterior summaries

Module #17
Bayesian Linear Regression
Theory and application of Bayesian linear regression, including model building and inference

Module #18
Bayesian Generalized Linear Models (GLMs)
Extension of Bayesian methods to GLMs, including binary and count outcomes

Module #19
Introduction to Joint Models for Longitudinal and Survival Data
Overview of joint models, including underlying assumptions and applications

Module #20
Joint Models for Longitudinal and Survival Data
Theory and application of joint models, including model building and interpretation

Module #21
Missing Data in Biostatistical Analysis
Overview of missing data mechanisms, including MAR, MCAR, and MNAR

Module #22
Multiple Imputation for Missing Data
Theory and application of multiple imputation, including model building and inference

Module #23
Sensitivity Analysis for Biostatistical Models
Overview of sensitivity analysis, including robustness and uncertainty

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Advanced Biostatistics for Medical Research: Biostatistical Modeling for Health Data career

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Advanced Biostatistics for Medical Research: Biostatistical Modeling for Health Data ( 24 Modules )

Advanced Biostatistics for Medical Research: Biostatistical Modeling for Health Data
( 24 Modules )