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Bayesian Methods in Clinical Biostatistics
( 25 Modules )

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


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