Module #1 Introduction to Survival Analysis Overview of survival analysis, importance in clinical trials, and common applications
Module #2 Basic Concepts in Survival Analysis Definition of survival data, types of censoring, and common notation
Module #3 Types of Survival Data Right-censored, left-censored, and interval-censored data, and their implications
Module #4 Descriptive Statistics for Survival Data Summary statistics for survival data, including Kaplan-Meier estimates and survival curves
Module #5 Kaplan-Meier Estimator In-depth explanation of the Kaplan-Meier estimator, including assumptions and limitations
Module #6 Nelson-Aalen Estimator Alternative estimator to Kaplan-Meier, including its advantages and disadvantages
Module #7 Survival Curve Comparison Methods for comparing survival curves, including log-rank test and hazard ratio
Module #8 Cox Proportional Hazards Model Introduction to Cox PH model, including underlying assumptions and_model interpretation
Module #9 Extended Cox Model Time-dependent variables, stratification, and interactions in Cox PH model
Module #10 Model Diagnostics and Assumptions Checking assumptions of Cox PH model, including proportional hazards and linearity
Module #11 Survival Regression Models Overview of alternative survival regression models, including accelerated failure time and frailty models
Module #12 Competing Risks Analysis Introduction to competing risks, including methods for estimation and inference
Module #13 Recurrent Events Analysis Introduction to recurrent events, including methods for estimation and inference
Module #14 Joint Models for Longitudinal and Survival Data Introduction to joint models, including shared parameter and Bayesian approaches
Module #15 Missing Data in Survival Analysis Methods for handling missing data in survival analysis, including imputation and inverse probability weighting
Module #16 Survival Analysis in Clustered Data Methods for accounting for clustering in survival analysis, including frailty models and generalized estimating equations
Module #17 Survival Analysis in Observational Studies Challenges and methods for survival analysis in observational studies, including propensity scores and instrumental variables
Module #18 Software for Survival Analysis Overview of popular software packages for survival analysis, including R, Python, and SAS
Module #19 Case Studies in Survival Analysis Real-world examples of survival analysis in clinical trials, including cancer, cardiovascular disease, and infectious disease
Module #20 Interpretation and Communication of Survival Analysis Results Best practices for interpreting and communicating survival analysis results to stakeholders
Module #21 Common Pitfalls and Challenges in Survival Analysis Common mistakes and challenges in survival analysis, and strategies for avoiding them
Module #22 Advanced Topics in Survival Analysis Recent developments and advanced topics in survival analysis, including machine learning and Bayesian methods
Module #23 Survival Analysis in Regulatory Settings Guidelines and regulations for survival analysis in clinical trials, including FDA and EMA guidelines
Module #24 Ethical Considerations in Survival Analysis Ethical considerations and biases in survival analysis, including issues of fairness and transparency
Module #25 Course Wrap-Up & Conclusion Planning next steps in Survival Analysis in Clinical Trials career
