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https://github.com/datastalker/survival-cox

This repository contains an R script for performing survival analysis on breast cancer surgery data from the University of Chicago's Billings Hospital. The analysis includes Kaplan-Meier estimation and Cox Proportional Hazards modeling to assess patient survival.
https://github.com/datastalker/survival-cox

breast-cancer-prediction cox-model data-analysis data-science data-visualization epidemiology kaplan-meier r survival-analysis

Last synced: 16 days ago
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This repository contains an R script for performing survival analysis on breast cancer surgery data from the University of Chicago's Billings Hospital. The analysis includes Kaplan-Meier estimation and Cox Proportional Hazards modeling to assess patient survival.

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README 

        
## Cox Model Survival Analysis



### About the Dataset

The dataset, `haberman.csv`, includes data from a study conducted between 1958 and 1970 at the University of Chicago's Billings Hospital. It focuses on the survival of breast cancer surgery patients.



- **Number of Instances:** 306

- **Number of Attributes:** 4

  - Age of patient at time of operation (numerical)

  - Patient's year of operation (year - 1900, numerical)

  - Number of positive axillary nodes detected (numerical)

  - Survival status (class attribute): 

    - `1` = Survived 5 years or longer

    - `2` = Died within 5 years

- **Missing Values:** None



### Files

- `haberman.csv`: The dataset used for survival analysis.

- `cox-survival.md`: R Markdown file for performing survival analysis.



### Usability

- **License:** Unknown

- **Expected Update Frequency:** Not specified



### Analysis Overview

1. **Introduction:** Explains the dataset and the objectives of the survival analysis.

2. **Load Required Libraries:** Lists and loads the libraries needed for the analysis.

3. **Data Import and Preview:** Imports the dataset and provides an initial preview.

4. **Data Profiling:** Provides diagnostic statistics for numeric variables.

5. **Defining Time and Event Variables:** Sets up variables for survival analysis.

6. **Kaplan-Meier Estimator:** Fits and plots the Kaplan-Meier survival curve.

7. **Stratified Kaplan-Meier Curves:** Creates and plots survival curves for different patient cohorts based on positive axillary nodes detected.

8. **Cox Proportional Hazards Model:** Fits a Cox model to the data and summarizes the results.

9. **Visualizing Cox Model Coefficients:** Generates a forest plot of Cox model coefficients.

10. **Predicting Survival Curves for Specific Patients:** Predicts and plots survival curves for selected patients.



### Example Code

Below is an example of loading the dataset and performing a Kaplan-Meier survival analysis:



```r

library(tidyverse)

library(survival)

library(survminer)

library(dlookr)

library(gridExtra)



# Load the dataset

data <- read.csv('haberman.csv', header = FALSE)

colnames(data) <- c('Age', 'Operation_year', 'Nb_pos_detected', 'Surv')



# Kaplan-Meier Estimator

km_fit <- survfit(Surv(Age, Surv) ~ 1, data = data)

ggsurvplot(km_fit, data = data, conf.int = FALSE, ggtheme = theme_minimal(), title = "Kaplan-Meier Estimate")

```



### Conclusion

The analysis explores patient survival probabilities using Kaplan-Meier estimation and Cox Proportional Hazards modeling. It provides insights into how various factors influence survival outcomes for breast cancer patients.



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