--- a
+++ b/demo/notebooks/CoxReg_Single_Run.ipynb
@@ -0,0 +1,97 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Survival Regression Model code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook describes code for the Cox Proportional Hazards regression used for the conventional parameter model in our study. We define a simple function that accepts as arguments the input data (predictor variables and survival outcomes) and a training parameter.\n",
+    "Below we describe each section of the code:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import required libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np, pandas as pd\n",
+    "import os, sys, time, pickle, copy, h5py\n",
+    "import lifelines.utils\n",
+    "from lifelines import CoxPHFitter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def coxreg_single_run(xtr, ytr, penalty):\n",
+    "    df_tr = pd.DataFrame(np.concatenate((ytr, xtr),axis=1))\n",
+    "    df_tr.columns = ['status','time'] + ['X'+str(i+1) for i in range(xtr.shape[1])]\n",
+    "    cph = CoxPHFitter(penalizer=penalty)\n",
+    "    cph.fit(df_tr, duration_col='time', event_col='status')\n",
+    "    return cph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Function arguments:\n",
+    "- `xtr` : $n \\times p$ matrix of predictor variables, where $n$ is sample size and $p$ is number of predictor variables. \n",
+    "- `ytr` : $n \\times 2$ matrix of survival outcomes, where first column is censoring status and second is survival/censoring time\n",
+    "- `penalty` : To control for high correlations among the variables, L2-norm regularization is used to restrict the size of the coefficient estimates, which improves their stability. The `penalty` coefficient controls the strength of the L2-norm regularization\n",
+    "\n",
+    "Function output:\n",
+    "- A `CoxPHFitter()` object which stores the model structure, regression coefficient estimates, etc. This object can be used to make predictions on new (test/validation) data"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}