Module #1 Introduction to Advanced Biostatistics Overview of the course, importance of biostatistics in healthcare, and review of basic statistical concepts
Module #2 Advanced Hypothesis Testing Review of hypothesis testing, type I and type II errors, and introduction to multiple testing corrections
Module #3 Non-Parametric Tests Introduction to non-parametric tests, Wilcoxon rank-sum test, and Kruskal-Wallis test
Module #4 Resampling Methods Introduction to resampling methods, bootstrap, and permutation tests
Module #5 Linear Regression Review of simple and multiple linear regression, residual analysis, and model diagnostics
Module #6 Generalized Linear Models Introduction to generalized linear models, logistic regression, and Poisson regression
Module #7 Survival Analysis Introduction to survival analysis, Kaplan-Meier estimator, and Cox proportional hazards model
Module #8 Time-to-Event Data Analysis of time-to-event data, censoring, and truncation
Module #9 Longitudinal Data Analysis Introduction to longitudinal data analysis, mixed effects models, and generalized estimating equations
Module #10 Non-Normal Data Analysis of non-normal data, transformations, and robust methods
Module #11 Correlated Data Analysis of correlated data, clustered data, and generalized linear mixed models
Module #12 Missing Data Introduction to missing data, types of missingness, and multiple imputation
Module #13 Meta-Analysis Introduction to meta-analysis, fixed and random effects models, and forest plots
Module #14 High-Dimensional Data Introduction to high-dimensional data, feature selection, and dimension reduction techniques
Module #15 Machine Learning in Biostatistics Introduction to machine learning, supervised and unsupervised learning, and model evaluation
Module #16 Genomics and Proteomics Introduction to genomics and proteomics, microarray analysis, and RNA-seq analysis
Module #17 Epidemiology and Biostatistics Introduction to epidemiology, study designs, and measures of disease frequency
Module #18 Clinical Trials Introduction to clinical trials, phases of clinical trials, and sample size calculation
Module #19 Ethics in Biostatistics Ethical considerations in biostatistics, informed consent, and data privacy
Module #20 Computational Tools in Biostatistics Introduction to computational tools in biostatistics, R, Python, and SAS
Module #21 Data Visualization in Biostatistics Introduction to data visualization, exploratory data analysis, and visualization best practices
Module #22 Statistical Computing Introduction to statistical computing, simulation, and Monte Carlo methods
Module #23 Big Data in Biostatistics Introduction to big data, data warehousing, and distributed computing
Module #24 Case Studies in Biostatistics Real-world case studies in biostatistics, applications, and critical thinking
Module #25 Course Wrap-Up & Conclusion Planning next steps in Advanced Biostatistics career
