Advanced Biostatistics for Medical Research: Bayesian Methods in Medical Research
( 30 Modules )
Module #1 Introduction to Bayesian Inference Overview of Bayesian statistics, Bayes theorem, and its application in medical research
Module #2 Prior Distributions and posterior inference Understanding prior distributions, posterior inference, and the importance of prior specification
Module #3 Bayesian vs Frequentist Statistics Comparison of Bayesian and frequentist approaches to statistical inference in medical research
Module #4 Introduction to Bayesian Linear Regression Bayesian approach to simple and multiple linear regression models
Module #5 Prior Specification for Regression Coefficients Specifying prior distributions for regression coefficients and interpreting results
Module #6 Bayesian Model Checking and Validation Checking model assumptions and validating Bayesian linear regression models
Module #7 Introduction to Bayesian GLMs Bayesian approach to logistic regression, Poisson regression, and other GLMs
Module #8 Prior Specification for GLMs Specifying prior distributions for GLMs and interpreting results
Module #9 Bayesian Model Comparison and Selection Comparing and selecting Bayesian GLMs using deviance information criterion (DIC) and cross-validation
Module #10 Introduction to Bayesian Survival Analysis Bayesian approach to survival analysis, including parametric and semi-parametric models
Module #11 Prior Specification for Survival Models Specifying prior distributions for survival models and interpreting results
Module #12 Bayesian Competing Risks Models Bayesian approach to competing risks models and interpreting results
Module #13 Introduction to Bayesian Longitudinal Data Analysis Bayesian approach to linear mixed effects models and generalized linear mixed models
Module #14 Prior Specification for Longitudinal Models Specifying prior distributions for longitudinal models and interpreting results
Module #15 Bayesian Non-Linear Mixed Effects Models Bayesian approach to non-linear mixed effects models and interpreting results
Module #16 Bayesian Model Averaging Bayesian model averaging and its application in medical research
Module #17 Bayesian Decision Analysis Bayesian decision analysis and its application in medical decision-making
Module #18 Bayesian Machine Learning Introduction to Bayesian machine learning and its application in medical research
Module #19 Introduction to MCMC Methods Markov Chain Monte Carlo (MCMC) methods for Bayesian inference
Module #20 Stan and RStan for Bayesian Modeling Introduction to Stan and RStan for Bayesian modeling and inference
Module #21 JAGS and rjags for Bayesian Modeling Introduction to JAGS and rjags for Bayesian modeling and inference
Module #22 Case Study:Bayesian Meta-Analysis Application of Bayesian meta-analysis in medical research
Module #23 Case Study:Bayesian Clinical Trials Application of Bayesian methods in clinical trials
Module #24 Case Study:Bayesian Epidemiology Application of Bayesian methods in epidemiological research
Module #25 Bayesian Non-Parametric Modeling Introduction to Bayesian non-parametric modeling and its application
Module #26 Bayesian Spatial Modeling Bayesian approach to spatial modeling and its application in medical research
Module #27 Bayesian Network Meta-Analysis Bayesian approach to network meta-analysis and its application in medical research
Module #28 Bayesian Methods for Missing Data Bayesian approach to handling missing data in medical research
Module #29 Bayesian Methods for Causal Inference Bayesian approach to causal inference and its application in medical research
Module #30 Course Wrap-Up & Conclusion Planning next steps in Advanced Biostatistics for Medical Research: Bayesian Methods in Medical Research career
