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

Module #1
Introduction to Advanced Biostatistics
Overview of advanced biostatistical methods, importance in medical research, and course objectives

Module #2
Review of Intermediate Biostatistics
Review of intermediate biostatistical methods, including linear regression, logistic regression, and survival analysis

Module #3
Generalized Linear Mixed Models (GLMMs)
Introduction to GLMMs, model specification, and estimation methods

Module #4
GLMMs Applications in Medical Research
Case studies and examples of GLMMs in medical research, including longitudinal data and cluster-randomized trials

Module #5
Joint Modeling of Longitudinal and Time-to-Event Data
Introduction to joint models, model specification, and estimation methods

Module #6
Joint Modeling Applications in Medical Research
Case studies and examples of joint models in medical research, including HIV/AIDS and cancer studies

Module #7
Machine Learning and Biostatistics
Introduction to machine learning, types of machine learning, and applications in biostatistics

Module #8
Supervised Learning Methods in Biostatistics
Introduction to supervised learning methods, including decision trees, random forests, and support vector machines

Module #9
Unsupervised Learning Methods in Biostatistics
Introduction to unsupervised learning methods, including clustering and dimensionality reduction techniques

Module #10
Biostatistical Applications of Deep Learning
Introduction to deep learning, convolutional neural networks, and recurrent neural networks in biostatistics

Module #11
Biostatistical Computing and Graphics
Introduction to biostatistical computing, including R and Python programming, and data visualization techniques

Module #12
Resampling Methods and Bootstrap
Introduction to resampling methods, including bootstrap and permutation tests

Module #13
Simulation-Based Inference
Introduction to simulation-based inference, including Monte Carlo methods and Markov chain Monte Carlo

Module #14
Advanced Survival Analysis
Introduction to advanced survival analysis, including frailty models and multi-state models

Module #15
Competing Risks and Multi-State Models
Introduction to competing risks and multi-state models, including estimation and inference methods

Module #16
Causal Inference in Medical Research
Introduction to causal inference, including confounding, bias, and causal diagrams

Module #17
Instrumental Variable Analysis
Introduction to instrumental variable analysis, including estimation and inference methods

Module #18
Mediation Analysis in Medical Research
Introduction to mediation analysis, including direct and indirect effects

Module #19
High-Dimensional Data Analysis
Introduction to high-dimensional data analysis, including feature selection and dimensionality reduction

Module #20
Genomic and Epigenomic Data Analysis
Introduction to genomic and epigenomic data analysis, including array-based and sequencing-based data

Module #21
Omics Data Integration and Analysis
Introduction to omics data integration and analysis, including multi-omics data analysis and genomics-driven medicine

Module #22
Bayesian Biostatistics
Introduction to Bayesian biostatistics, including Bayesian inference and Markov chain Monte Carlo

Module #23
Bayesian Regression Modeling
Introduction to Bayesian regression modeling, including hierarchical models and Bayesian generalized linear models

Module #24
Advanced Topics in Biostatistics
Special topics in biostatistics, including missing data, measurement error, and correlated data

Module #25
Ethics and Regulatory Considerations in Biostatistics
Ethical considerations in biostatistics, including confidentiality, data sharing, and informed consent

Module #26
Software and Computing in Biostatistics
Overview of biostatistical software, including R, Python, and SAS

Module #27
Collaboration and Communication in Biostatistics
Effective collaboration and communication strategies in biostatistics, including interdisciplinary research and publication

Module #28
Case Studies in Advanced Biostatistics
Real-world case studies and examples of advanced biostatistical methods in medical research

Module #29
Advanced Biostatistics Project Development
Development of individual or group projects applying advanced biostatistical methods to a medical research problem

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Advanced Biostatistics for Medical Research: Advanced Statistical Methods in Medical Research career

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

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