Biostatistics for Clinical Research
( 27 Modules )

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 #9
Survival Analysis
Kaplan-Meier estimates, Cox proportional hazards model, and survival curve interpretation

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

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Biostatistics for Clinical Research ( 27 Modules )

Biostatistics for Clinical Research
( 27 Modules )