Introduction to Advanced Biostatistics
( 25 Modules )

Module #1
Introduction to Advanced Biostatistics
Overview of the course, importance of biostatistics in healthcare, and course objectives

Module #2
Review of Basic Biostatistics
Revision of basic concepts in biostatistics, including types of data, probability, and statistical inference

Module #3
Hypothesis Testing and Confidence Intervals
Advanced hypothesis testing, including multiple comparisons and interaction effects, and construction of confidence intervals

Module #4
Regression Analysis
Simple and multiple linear regression, regression diagnostics, and model building strategies

Module #5
Logistic Regression
Introduction to logistic regression, odds ratios, and logistic regression modeling

Module #6
Survival Analysis
Introduction to survival analysis, Kaplan-Meier curves, and Cox proportional hazards model

Module #7
Time-to-Event Analysis
Advanced topics in survival analysis, including censoring, truncation, and competing risks

Module #8
Recurrent Events Analysis
Analysis of recurrent events, including Poisson and negative binomial models

Module #9
Clustered and Longitudinal Data Analysis
Introduction to clustered and longitudinal data analysis, including mixed effects models and generalized estimating equations

Module #10
Meta-Analysis
Introduction to meta-analysis, including fixed and random effects models, and forest plots

Module #11
Non-Parametric Statistics
Introduction to non-parametric statistics, including Wilcoxon rank-sum test and Kruskal-Wallis test

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

Module #13
Machine Learning in Biostatistics
Introduction to machine learning in biostatistics, including supervised and unsupervised learning

Module #14
High-Dimensional Data Analysis
Introduction to high-dimensional data analysis, including principal component analysis and clustering

Module #15
Bioinformatics and Genomics
Introduction to bioinformatics and genomics, including microarray analysis and genome-wide association studies

Module #16
Case-Control Studies
Design and analysis of case-control studies, including matching and stratification

Module #17
Cohort Studies
Design and analysis of cohort studies, including confounding and effect modification

Module #18
Cross-Sectional Studies
Design and analysis of cross-sectional studies, including sampling and bias

Module #19
Experimental Design
Introduction to experimental design, including randomized controlled trials and crossover designs

Module #20
Survey Research Methods
Introduction to survey research methods, including sampling and non-response bias

Module #21
Missing Data Analysis
Introduction to missing data analysis, including multiple imputation and maximum likelihood estimation

Module #22
Sensitivity Analysis
Introduction to sensitivity analysis, including deterministic and probabilistic sensitivity analysis

Module #23
Ethics in Biostatistics
Ethical considerations in biostatistics, including data privacy and informed consent

Module #24
Communicating Biostatistical Results
Effective communication of biostatistical results, including data visualization and reporting

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Introduction to Advanced Biostatistics career

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Introduction to Advanced Biostatistics ( 25 Modules )

Introduction to Advanced Biostatistics
( 25 Modules )