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
