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
