Advanced Statistical Methods in Clinical Research
( 25 Modules )

Module #1
Introduction to Advanced Statistical Methods
Overview of advanced statistical methods in clinical research, importance of advanced methods, and course objectives

Module #2
Review of Statistical Inference
Review of hypothesis testing, confidence intervals, and p-values

Module #3
Linear Regression:Model Building and Diagnostics
Advanced linear regression techniques, model building, and diagnostic methods

Module #4
Generalized Linear Models (GLMs)
Introduction to GLMs, logistic regression, and Poisson regression

Module #5
GLMs:Model Building and Diagnostics
GLM model building, diagnostics, and interpretation

Module #6
Survival Analysis:Introduction and Kaplan-Meier Estimates
Introduction to survival analysis, Kaplan-Meier estimates, and censoring

Module #7
Survival Analysis:Cox Regression and Model Building
Cox proportional hazards regression, model building, and diagnostics

Module #8
Time-to-Event Analysis:Advanced Topics
Competing risks, recurrent events, and multistate models

Module #9
Longitudinal Data Analysis:Introduction and Linear Mixed Effects Models
Introduction to longitudinal data analysis, linear mixed effects models, and model building

Module #10
Longitudinal Data Analysis:Generalized Linear Mixed Models
Generalized linear mixed models, including binary and count outcomes

Module #11
Missing Data in Clinical Research
Introduction to missing data, types of missingness, and simple imputation methods

Module #12
Multiple Imputation for Missing Data
Multiple imputation methods, including Bayesian and frequentist approaches

Module #13
Causal Inference in Clinical Research
Introduction to causal inference, confounding, and bias

Module #14
Causal Inference:Instrumental Variables and Regression Discontinuity
Instrumental variables, regression discontinuity design, and sensitivity analysis

Module #15
Machine Learning in Clinical Research
Introduction to machine learning, supervised and unsupervised learning, and model evaluation

Module #16
Machine Learning:Advanced Topics
Deep learning, neural networks, and natural language processing in clinical research

Module #17
Bayesian Methods in Clinical Research
Introduction to Bayesian methods, prior distributions, and posterior inference

Module #18
Bayesian Methods:Advanced Topics
Bayesian model selection, Bayesian non-parametrics, and Bayesian computational methods

Module #19
Cluster-Randomized Trials and Stepped Wedge Designs
Design and analysis of cluster-randomized trials and stepped wedge designs

Module #20
Adaptive Clinical Trials and Seamless Designs
Adaptive clinical trials, seamless designs, and phase II-III trials

Module #21
Implementation of Advanced Statistical Methods
Implementation of advanced statistical methods in R, Python, or other software

Module #22
Interpretation and Communication of Advanced Statistical Results
Interpretation and communication of advanced statistical results in clinical research

Module #23
Advanced Statistical Methods in Real-World Data
Application of advanced statistical methods to real-world data in clinical research

Module #24
Ethical Considerations in Advanced Statistical Methods
Ethical considerations in the application of advanced statistical methods in clinical research

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Advanced Statistical Methods in Clinical Research career

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Advanced Statistical Methods in Clinical Research ( 25 Modules )

Advanced Statistical Methods in Clinical Research
( 25 Modules )