This repository includes the data and codes for the manuscript "Predicting survival of NSCLC patients treated with immune checkpoint inhibitors: Impact and timing of immune-related adverse events and prior tyrosine kinase inhibitor therapy" by Sayer et al.

##Description of the data files

1. `combined.data.csv` dataset contains all data from 354 patients used
2. `training.data.csv` dataset contains data from 283 patients used as the training datasets for the predictive modeling
3. `testing.data.csv` dataset contains data from 71 patients used as the testing datasets for the predictive modeling
4. `NSCLC Data Dictionary` contains the description of the variables used in the datasets

##Description of the code files 1. NSCLC_code.R contains the R codes for generating the figures in the manuscript using the datasets. Install the following R packages, which can be obtained using either the install.packages() function in R or via the Bioconductor framework.

	* glmulti
	* survcomp
	* finalfit
	* caret
	* pROC
	* ROCR
	* MLmetrics
	
2. '''NSCLC_Machine_Learning''' contains the python codes for generating the figures in the manuscript related to machine learning models. Within the folder there 4 scripts that detail distinct steps in the process. Additonally, there is a README file elaborating further on the purpose of each of the prospective files.
	* step01_data_cleaning.ipynb
	* step02_exploratory_analysis.ipynb
	* step03_predictive_modeling_os.ipynb
	* step04_predictive_modeling_pfs.ipynb

Contact information

• Moom Roosan, PharmD, PhD [roosan@chapman.edu]
• Ravi Salgia, MD, PhD [rsalgia@coh.org]