1. Introduction to Survival Analysis                                  

         1.1 Why Use Survival Analysis? . . . . . . . . . . . . . . . . . . . . . . . . . . .
               1.1.1 Using R to do the Analysis  
               1.1.2 Visualizing the Data Structure 
         1.2 Analysis with the Kaplan-Meier Curve

2. Defining Your Research Question and Setting Up a Real Dataset in R  
          2.1 Why a Good Research Question is Important? 
          2.2  Examples and Elements of Good Survival Analysis Questions 
          2.3  Setting Up a Real Dataset in R 
          2.4  Preparing the Data for Survival Analysis 
          2.5  Analyzing the Sex Variable Using Kaplan–Meier Curves

Chapter Summary       Exploring Your Dataset, Handling Missing Data and Making Preliminary Visualizations in R

        2.6 Why Explore the Data First? 
        2.7 Quick Overview of the Dataset 
        2.8 Checking Missing Data
        2.9 Handling Missing Data 
               2.9.1 Complete-case analysis 
               2.9.2 Multiple Imputation of Missing Data 
        2.10 Exploring Key Variables 
        2.11 Chapter Summary

3. Kaplan-Meier Survival Analysis in Detail    
         3.1 Why Use Kaplan-Meier?
         3.2 KM with One Group 
         3.3  KM with Two or More Groups 
         3.4 Comparing Groups: The Log-Rank Test 
         3.5  Extracting Median Survival and Confidence Intervals

Chapter Summary Cox Proportional Hazards Regression                
         3.6 What is the Cox Proportional Hazards Model? 
         3.7 Hazard Ratios (HR) in Plain Language 
         3.8 Fitting a Cox Model in R  
         3.9 Adding More Variables 
         3.10 Checking the Proportional Hazards Assumption 
         3.11 Plotting Adjusted Survival Curves 
         3.12 Exporting Results for Reports 
         3.13 Chapter Summary

6 Checking and Improving Model Fit 30

         6.1 Why Model Diagnostics Matter? 
         6.2 Detecting Influential Observations 
         6.3 Checking Overall Model Fit 
         6.4 Variable Selection 
         6.5 Testing for Multicollinearity  
         6.6 Alternative Approaches if PH Assumption Fails 
               6.6.1 Stratified Cox Proportional Hazards Model  
               6.6.2 Time-varying effects in the Cox model 
               6.6.3 Fitting the Piecewise Cox Model 
               6.6.4 Interpreting the Results 
               6.6.5 Why Use a Piecewise Cox Model? 
               6.6.6 Checking the Updated Model 
       6.7 Final Checklist Before Reporting

7 Parametric Models 44

        7.1 Why use parametric models?

        7.2 Common distributions in survival analysis

        7.3 Fitting a Weibull model in R

        7.4 Fitting an Exponential Model

        7.5 Plotting Parametric vs. KM Curves

Chapter Summary

8 Frailty Models 49

         8.1 Concept of Frailty

         8.2 Shared Frailty Models

               8.2.1 Example Using the Lung Dataset

              8.2.2 Fitting a Shared Frailty Model

         8.3 Standard Cox Model (Baseline) for Comparison

         8.4 Parametric Frailty Model: Weibull with Shared Effect

         8.5 Alternative: Clustered Robust Standard Errors

Chapter Summary

9 Presenting and Reporting Results 55

         9.1 Essential Elements of a Survival Analysis Report

         9.2 Creating Clean Tables in R

         9.3 Exporting Tables for Reports

         9.4 Producing Publication-Ready Survival Plots

         9.5 Writing Plain-Language Interpretations

10 Writing the Final Project Report

         10.1 Recommended Report Structure

         10.2 Integrating R Outputs into the Report

         10.3 Writing Style Tips

         10.4 Final Checks Before Submission
11 Using Software Tools: R (survival, survminer) & Python (lifelines) — Exporting, and Debugging
           11.1 Quick overview / checklist
           11.2 R — step-by-step (survival + survminer)
                     11.2.1 Installing & Loading packages in R
                     11.2.2 Loading data in R (example: CSV)
                     11.2.3 Preparing the ‘Surv‘ object & quick checks
                     11.2.4 Kaplan-Meier in R
                     11.2.5 The Cox model in R
                     11.2.6 Diagnostics for the Cox model
                     11.2.7 Exporting graphs & tables (R)
           11.3 Python — step-by-step (lifelines)
                     11.3.1 Installing & importing Python packages
                    11.3.2 Loading data into the Python environment
                    11.3.3 Kaplan–Meier analysis in Python
                    11.3.4 Grouped Kaplan–Meier analysis in Python
                    11.3.5 Cox proportional hazards modeling in Python
                    11.3.6 Exporting tables and plots in Python
           11.4 Reproducible notebooks in Python
           11.5 Common problems and troubleshooting in R and Python
                   11.5.1 Problem A — Event coding issues in R and Python
                   11.5.2 Problem B — “Time must be non-negative” or negative durations
                   11.5.3 Problem C — ggsurvplot() not found or blank plot
                   11.5.4 Problem D — Cox model fails to converge or gives warnings
                   11.5.5 Problem E — PH assumption violations
                   11.5.6 Problem F — Missing values or dropped rows
                   11.5.7 Problem G — Unstable hazard ratios or wide confidence intervals
                   11.5.8 Problem H — Memory errors with large datasets
           11.6 Debugging Workflow (How to Approach an Error)
           11.7 Example: Minimal Reproducible Script in R and Python
           11.8 Final Tips for Reproducible Work
Chapter Summary
Conclusion