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

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Advanced Biostatistics for Medical Research: Bayesian Methods in Medical Research ( 30 Modules )

Advanced Biostatistics for Medical Research: Bayesian Methods in Medical Research
( 30 Modules )