Biostatistics for Clinical Trials
( 30 Modules )

Module #1
Introduction to Biostatistics in Clinical Trials
Overview of biostatistics, clinical trials, and their intersection

Module #2
Types of Clinical Trials
Different types of clinical trials, including Phase I-IV, observational, and randomized controlled trials

Module #3
Basic Statistical Concepts
Review of basic statistical concepts, including hypothesis testing, confidence intervals, and p-values

Module #4
Descriptive Statistics and Data Visualization
Descriptive statistics, data visualization, and exploratory data analysis techniques

Module #5
Probability and Sampling Distributions
Probability theory, sampling distributions, and the central limit theorem

Module #6
Hypothesis Testing
Hypothesis testing, including type I and II errors, power, and sample size calculations

Module #7
Confidence Intervals
Confidence intervals, including construction, interpretation, and applications

Module #8
t-tests and ANOVA
t-tests, ANOVA, and other parametric tests for comparing means

Module #9
Non-Parametric Tests
Non-parametric tests, including Wilcoxon rank-sum test and Kruskal-Wallis test

Module #10
Regression Analysis
Simple and multiple regression analysis, including model building and interpretation

Module #11
Clinical Trial Design Principles
Key design principles, including randomization, stratification, and blinding

Module #12
Sample Size Calculation
Sample size calculation methods, including formula-based approaches and simulation-based approaches

Module #13
Intention-to-Treat (ITT) and Per-Protocol (PP) Analysis
ITT and PP analysis, including benefits, limitations, and applications

Module #14
Survival Analysis
Survival analysis, including Kaplan-Meier curves and Cox proportional hazards model

Module #15
Missing Data and Imputation
Handling missing data, including types of missingness, imputation methods, and sensitivity analysis

Module #16
Longitudinal Data Analysis
Longitudinal data analysis, including linear mixed effects models and generalized estimating equations

Module #17
Generalized Linear Mixed Models (GLMMs)
GLMMs, including binomial and Poisson regression with random effects

Module #18
Machine Learning and Predictive Modeling
Introduction to machine learning and predictive modeling in clinical trials

Module #19
High-Dimensional Data Analysis
Analyzing high-dimensional data, including gene expression and omics data

Module #20
Meta-Analysis and Systematic Reviews
Meta-analysis and systematic reviews, including pooling data and assessing heterogeneity

Module #21
Ethical Considerations in Clinical Trials
Ethical principles, including informed consent, confidentiality, and equipoise

Module #22
Good Clinical Practice (GCP) and Regulatory Considerations
GCP, regulatory guidelines, and international standards

Module #23
Data Monitoring Committees (DMCs) and Interim Analysis
DMCs, interim analysis, and adaptive trial designs

Module #24
Subgroup Analysis and Biomarkers
Subgroup analysis, biomarkers, and personalized medicine

Module #25
Clinical Trial Reporting and Publishing
Reporting and publishing clinical trial results, including CONSORT and SPIRIT guidelines

Module #26
Case Study:Phase III Clinical Trial
Real-world example of a Phase III clinical trial, including design, analysis, and interpretation

Module #27
Case Study:Adaptive Clinical Trial Design
Example of an adaptive clinical trial design, including simulation-based optimization

Module #28
Biostatistical Consulting in Clinical Trials
Roles and responsibilities of biostatisticians in clinical trials, including collaboration with clinicians and regulators

Module #29
Final Project:Design and Analysis of a Clinical Trial
Students design and analyze a clinical trial, applying concepts learned throughout the course

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Biostatistics for Clinical Trials career

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Biostatistics for Clinical Trials ( 30 Modules )

Biostatistics for Clinical Trials
( 30 Modules )