Survival Analysis in Clinical Trials
( 25 Modules )

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

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Survival Analysis in Clinical Trials ( 25 Modules )

Survival Analysis in Clinical Trials
( 25 Modules )