Statistical Methods in Biomedical Research
( 30 Modules )

Module #1
Introduction to Biostatistics
Overview of biostatistics, importance in biomedical research, and common applications

Module #2
Descriptive Statistics
Summary statistics, data visualization, and exploratory data analysis

Module #3
Probability Theory
Basic concepts of probability, conditional probability, and Bayes theorem

Module #4
Random Variables and Distributions
Types of random variables, probability distributions, and common statistical distributions

Module #5
Inferential Statistics
Hypothesis testing, confidence intervals, and p-values

Module #6
One-Sample Tests
Z-tests, t-tests, and Wilcoxon signed-rank test for one-sample inference

Module #7
Two-Sample Tests
Independent samples t-test, paired t-test, and non-parametric tests for two-sample inference

Module #8
ANOVA and Regression
Analysis of variance (ANOVA), simple and multiple linear regression

Module #9
Correlation and Covariance
Measures of association, correlation coefficient, and covariance analysis

Module #10
Categorical Data Analysis
Chi-squared tests, Fishers exact test, and contingency tables

Module #11
Survival Analysis
Kaplan-Meier estimator, survival curves, and Cox proportional hazards model

Module #12
Longitudinal Data Analysis
Repeated measures ANOVA, linear mixed effects models, and generalized estimating equations

Module #13
Clustered Data Analysis
Clustered data, intra-cluster correlation, and generalized linear mixed models

Module #14
Missing Data Analysis
Types of missing data, missing data mechanisms, and imputation methods

Module #15
Meta-Analysis
Combining results from multiple studies, fixed and random effects models

Module #16
Machine Learning in Biomedical Research
Introduction to machine learning, supervised and unsupervised learning, and evaluation metrics

Module #17
High-Dimensional Data Analysis
Dimension reduction techniques, feature selection, and clustering methods

Module #18
Genomic and Proteomic Data Analysis
Microarray analysis, RNA-seq analysis, and proteomic data analysis

Module #19
Image Analysis in Biomedical Research
Image processing, feature extraction, and image segmentation

Module #20
Ethics and Regulatory Considerations
Ethical principles, informed consent, and regulatory requirements in biomedical research

Module #21
Case Studies in Biomedical Research
Real-world examples of statistical applications in biomedical research

Module #22
Statistical Software in Biomedical Research
Overview of popular statistical software, including R, Python, and SAS

Module #23
Data Management and Visualization
Data cleaning, data transformation, and data visualization best practices

Module #24
Collaboration and Communication
Effective collaboration with researchers, clinicians, and stakeholders, and clear communication of statistical results

Module #25
Advanced Topics in Biostatistics
Current trends and advanced topics in biostatistics, including Bayesian methods and machine learning

Module #26
Final Project
Applied project in statistical methods in biomedical research

Module #27
Additional Topics in Biostatistics
Optional topics, including spatial statistics, network analysis, and computational biology

Module #28
Specialized Biostatistical Methods
Advanced biostatistical methods, including multilevel modeling, latent class analysis, and instrumental variables

Module #29
Biostatistical Consulting
Best practices for consulting with researchers, clinicians, and industry partners

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Statistical Methods in Biomedical Research career

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Statistical Methods in Biomedical Research ( 30 Modules )

Statistical Methods in Biomedical Research
( 30 Modules )