Module #1 Introduction to Bayesian Statistics Overview of Bayesian philosophy, Bayes theorem, and contrast with frequentist statistics
Module #2 Foundations of Probability Review of probability theory, including conditional probability, independence, and Bayes rule
Module #3 Bayesian Inference Introduction to Bayesian inference, including posterior distributions and posterior summaries
Module #4 Conjugate Priors Introduction to conjugate priors, including beta-binomial and normal-normal models
Module #5 Bayesian Linear Regression Introduction to Bayesian linear regression, including posterior inference and model checking
Module #6 Hierarchical Modeling Introduction to hierarchical models, including multilevel and mixed effects models
Module #7 Bayesian Model Selection Introduction to Bayesian model selection, including Bayes factors and posterior model probabilities
Module #8 Markov Chain Monte Carlo (MCMC) Introduction to MCMC methods for posterior inference, including Gibbs sampling and Metropolis-Hastings
Module #9 Bayesian Computation in R Introduction to Bayesian computation in R, including the use of MCMCpack and rstan
Module #10 Clinical Trial Design Introduction to Bayesian clinical trial design, including sample size determination and adaptive designs
Module #11 Bayesian Methods for Binary Outcomes Bayesian approaches for analyzing binary outcomes, including logistic regression and probit models
Module #12 Bayesian Methods for Survival Analysis Bayesian approaches for survival analysis, including proportional hazards models and cure rate models
Module #13 Bayesian Methods for Longitudinal Data Bayesian approaches for longitudinal data, including linear mixed effects models and generalized linear mixed models
Module #14 Bayesian Methods for Time-to-Event Data Bayesian approaches for time-to-event data, including Cox PH models and accelerated failure time models
Module #15 Bayesian methods for High-Dimensional Data Bayesian approaches for high-dimensional data, including Bayesian shrinkage methods and sparse models
Module #16 Bayesian Methods for Cluster-Randomized Trials Bayesian approaches for cluster-randomized trials, including Bayesian meta-analysis and network meta-analysis
Module #17 Bayesian Methods for Missing Data Bayesian approaches for missing data, including multiple imputation and Bayesian model averaging
Module #18 Bayesian Methods for Observational Studies Bayesian approaches for observational studies, including propensity scores and instrumental variable analysis
Module #19 Bayesian Methods for Personalized Medicine Bayesian approaches for personalized medicine, including Bayesian decision theory and value of information analysis
Module #20 Bayesian Methods for Regulatory Approval Bayesian approaches for regulatory approval, including Bayesian decision theory and probabilistic safety metrics
Module #21 Bayesian Methods for Real-World Evidence Bayesian approaches for real-world evidence, including Bayesian meta-analysis and predictive modeling
Module #22 Bayesian Methods for Safety Monitoring Bayesian approaches for safety monitoring, including Bayesian sequential methods and predictive modeling
Module #23 Bayesian Methods for Benefit-Risk Assessment Bayesian approaches for benefit-risk assessment, including Bayesian decision theory and value of information analysis
Module #24 Case Studies in Bayesian Biostatistics Real-world case studies illustrating the application of Bayesian methods in clinical biostatistics
Module #25 Course Wrap-Up & Conclusion Planning next steps in Bayesian Methods in Clinical Biostatistics career
