Module #1 Introduction to Biostatistics Overview of biostatistics, importance in clinical research, and role of biostatisticians
Module #2 Types of Clinical Research Studies Exploratory, observational, experimental, and quasi-experimental studies; cross-sectional, longitudinal, and case-control studies
Module #3 Study Designs and Methodologies Randomized controlled trials (RCTs), cohort studies, case-control studies, and cross-sectional studies
Module #4 Descriptive Statistics Measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation)
Module #5 Inferential Statistics Hypothesis testing, confidence intervals, p-values, and types of errors
Module #6 Probability and Statistics Review Review of probability theory, distributions (Bernoulli, binomial, Poisson, normal, t-distributions), and statistical inference
Module #7 Data Visualization Introduction to data visualization, types of plots (histograms, box plots, scatter plots), and visualization best practices
Module #8 Data Preprocessing and Cleaning Handling missing data, data transformations, data quality control, and data cleaning techniques
Module #10 Linear Regression Simple and multiple linear regression, model assumptions, and model building strategies
Module #11 Logistic Regression Binary logistic regression, model interpretation, and model building strategies
Module #12 Generalized Linear Models GLM theory, Poisson regression, and generalized linear mixed models
Module #13 Time-to-Event Analysis Parametric and non-parametric methods for time-to-event data, and competing risks
Module #14 Longitudinal Data Analysis Introduction to longitudinal data analysis, linear mixed effects models, and generalized estimating equations
Module #15 Clustered and Correlated Data Clustered data, correlated data, and methods for analysis (GLMM, GEE)
Module #16 Missing Data and Imputation Introduction to missing data, types of missingness, and imputation methods (mean, regression, multiple imputation)
Module #17 Causal Inference Introduction to causal inference, confounding, and causal effect estimation (propensity scores, instrumental variables)
Module #18 Meta-Analysis Introduction to meta-analysis, fixed-effects and random-effects models, and meta-analysis applications
Module #19 Non-Inferiority and Equivalence Trials Design and analysis of non-inferiority and equivalence trials, and margins
Module #20 Cluster Randomized Trials Design and analysis of cluster randomized trials, and accounting for clustering
Module #21 Adaptive Clinical Trials Introduction to adaptive clinical trials, design and analysis, and challenges
Module #22 Biomarkers and Surrogate Endpoints Introduction to biomarkers and surrogate endpoints, validation, and application in clinical trials
Module #23 Real-World Evidence and Observational Studies Introduction to real-world evidence, observational studies, and causal inference methods
Module #24 Regulatory Considerations in Clinical Trials Regulatory requirements, good clinical practice, and ethics in clinical research
Module #25 Data Management and Quality Control Data management, data quality control, and data validation in clinical trials
Module #26 Communication of Biostatistical Results Effective communication of biostatistical results, reporting guidelines, and presentation best practices
Module #27 Course Wrap-Up & Conclusion Planning next steps in Biostatistics for Clinical Research career
