example_notebooks/SurvivalAnalysisExample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What you will learn from this notebook\n",
    "\n",
    "This notebook is supposed to demonstrate a simplified version of an actual analysis you might want to run. In the real world steps would be probably the same but the dataset itself would be much, much noisier (meaning it would take some effort to put it into the required shape) and much bigger (I mean, nowadays in the industry we are dealing with more than ~30 samples!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# general packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# specialized stats packages\n",
    "from lifelines import KaplanMeierFitter\n",
    "\n",
    "# plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# preferences\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will use one of default datasets from lifetimes library. I don't know much about it and would prefer to avoid jumping to conclusions so I will pretend this data comes actually from a survey among 26 vampires collected a 100 years ago. \n",
    "\n",
    "In that survey scientists collected information about how many years ago the vampire became un-dead (in other words was bitten by another vampire and turned into one), how old they were at the time of their transformation, whether they identified themselves as binary or non-binary and whether they have experienced depression symptoms yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# data\n",
    "from lifelines.datasets import load_psychiatric_patients\n",
    "\n",
    "df = load_psychiatric_patients()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>T</th>\n",
       "      <th>C</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age   T  C  sex\n",
       "0   51   1  1    2\n",
       "1   58   1  1    2\n",
       "2   55   2  1    2\n",
       "3   28  22  1    2\n",
       "4   21  30  0    1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, so we have vampires at different age when they tranformed (`Age` column), they reported how many years have passed since transformation (`T` column), whether they have experienced depression symptoms (`C` column) and what gender they identify with (`sex` column, I'm gonna assume `1` is binary and `2` is non-binary because why not)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting lifetimes and very basic data exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There aren't many variables to work with and I will first show you how to plot lifetimes (assuming *now* is at 25, change `current_time` to see how the plot changes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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B0JKWugkuXryYKVOmMGLECHr37k2vXr245JJLeP/99xu6cfbo0YMpU6Zw1113NZxNfvDB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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "current_time = 25\n",
    "observed_lifetimes = df['T'].values\n",
    "observed = observed_lifetimes < current_time\n",
    "\n",
    "# I'm using slightly modified function from lifetimes library. See the end of this notebook for details.\n",
    "# If you are running this notebook yourself first execute the cell with function definition at the bottom\n",
    "# of this notebook\n",
    "plot_lifetimes(observed_lifetimes, event_observed=observed, block=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next I will see whether experiencing depression symptoms is somehow related to age at which the transformation into a vampire took place:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jz0pcD/w38LZmYuzur9R3By0pJH0V+DXwkYiYWnY8ZjZ45NO9s4BjI2Kv/h7/gLkmYGY2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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"C\", y=\"Age\", kind=\"boxen\",\n",
    "            data=df.sort_values(\"C\"));\n",
    "plt.xlabel('Vampire experienced depression or not', size=18)\n",
    "plt.ylabel('Age', size=18)\n",
    "plt.title('Vampire survival as a function of age', size=18);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like it does! Appears that vampires who have experienced depressive symptoms were on average older when they were bitten and consequently turned into vampires. This is very interesting! Let's look at Kaplan-Meier curves, and hazard curves to check whether gender has anything to do with depressive symptoms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaplan-Meier curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmf = KaplanMeierFitter()\n",
    "T = df[\"T\"] # time since vampire transformation\n",
    "C = df[\"C\"] # whether they experienced depression symptoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmf.fit(T,C);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5dfc6a4d68>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jd8ctbxisG++5SZ5bVbdU1a1VdXqvAgEAZopeD6/vl+ToJC9NsjjJzVV1fGtty/hBVXVe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\nKwCATgQrAIBOBCsAgE4EKwCATv4/4xgg6h03CygAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf.survival_function_\n",
    "kmf.median_\n",
    "kmf.plot(figsize=[10,6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kaplan-Meier curve plotted separately for vampires who define themselves as binary and non-binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6250eAYYlpwIBABoRVgAAjQgrAIBGhBUAQCMuXmdYGzemK8se7u70GEmS7jVr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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot both genders on the same plot\n",
    "plt.figure(figsize=[10,6])\n",
    "\n",
    "groups = df['sex']\n",
    "ix = (groups == 1)\n",
    "\n",
    "kmf.fit(T[~ix], C[~ix], label='binary vampires')\n",
    "ax = kmf.plot(figsize=[10,10]);\n",
    "\n",
    "kmf.fit(T[ix], C[ix], label='non-binary vampires')\n",
    "kmf.plot(ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our sample size is small so error bars are relatively large. It looks like in the early years after vampire tranformation more binary (blue line) than non-binary (orange line) vampires experienced depressive symptoms. Maybe non-binary vampires were in a honeymoon stage with vampirism? However, the error bars are pretty much overlapping starting at 20 years past transformation so likely the differences are not statistically significant. But let's look at the hazard rate first."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hazard rate using Nelson-Aalen estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import NelsonAalenFitter\n",
    "naf = NelsonAalenFitter()\n",
    "\n",
    "naf.fit(T,event_observed=C);\n",
    "naf.plot(figsize=[10,6]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9X7xiVn7x859lm222We2Pb7PNNnn+C16QW+bOzdS9piVJDj3s8Jx37qez5+RJed7znp+9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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "naf.fit(T[~ix], C[~ix], label='binary vampires')\n",
    "ax = naf.plot(figsize=[10,10])\n",
    "\n",
    "naf.fit(T[ix], C[ix], label='non-binary vampires')\n",
    "naf.plot(ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, so it looks like hazard rate increases with time for both groups which we could already deduce from survival curves. Interestingly, it seems that the hazard rate for non-binary vampires increases rapidly around 35 years compared to previous period (I'm ignoring error bars for the moment)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical analysis of differences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there a difference between hazard rate for binary and non-binary vampires? Let's run a log rank test. It will look at random combinations of samples from the two distributions and calculate how many times one had a higher value than the other.\n",
    "\n",
    "A very important point to remember is that this analysis will not tell us anything about the hazard rates themselves but rather whether one is different from the other - so it signals only relative differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results\n",
      "   t 0: -1\n",
      "   test: logrank\n",
      "   alpha: 0.95\n",
      "   null distribution: chi squared\n",
      "   df: 1\n",
      "\n",
      "   __ p-value ___|__ test statistic __|____ test result ____|__ is significant __\n",
      "         0.20485 |              1.607 |  Cannot Reject Null |       False       \n"
     ]
    }
   ],
   "source": [
    " from lifelines.statistics import logrank_test\n",
    "\n",
    " results = logrank_test(T[ix], T[~ix], event_observed_A=C[ix], event_observed_B=C[~ix])\n",
    " results.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like indeed there are no significant differences between binary and non-binary vampires but for the sake of exercise let's see how to get from the test statistic to difference in hazard rate:\n",
    "\n",
    "$$ log{\\lambda} = Z \\sqrt{ \\frac{4}{D} }  $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.098143897765532"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = results.test_statistic\n",
    "D = C.count()\n",
    "\n",
    "log_lambda = Z * np.sqrt (D / 4)\n",
    "log_lambda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, so if the test was significant we could conclude that the hazard rate for binary versus non-binary vampires is roughly 4 times higher which means they are more likely to suffer from depressive symptoms "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What factors influence vampire's survival? Cox Proportional Hazards Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, and lets say now we want to look at how age and gender identity shape vampire's future. We want to train the model on one set of samples and then use it to predict relative hazard increases (it's always relative to other vampires, never absolute hazard!) during vampire's lifetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convergence completed after 5 iterations.\n",
      "n=26, number of events=14\n",
      "\n",
      "       coef  exp(coef)  se(coef)       z      p  lower 0.95  upper 0.95     \n",
      "Age  0.2075     1.2306    0.0583  3.5606 0.0004      0.0933      0.3218  ***\n",
      "sex -0.5237     0.5923    0.7375 -0.7101 0.4776     -1.9696      0.9221     \n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n",
      "\n",
      "Concordance = 0.816\n"
     ]
    }
   ],
   "source": [
    "from lifelines import CoxPHFitter\n",
    "\n",
    "cph = CoxPHFitter()\n",
    "cph.fit(df, duration_col='T', event_col='C', show_progress=True)\n",
    "\n",
    "cph.print_summary()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like age is significantly related to the occurence of depressive symptoms, just like our EDA indicated at the beginning. If we had some new data we could use the beta values calculated in by fitting method in the previous step to predict relative changes in hazard rates of new vampires (using `cph.predict_cumulative_hazard(new_df)`. This is a semi-parametric model which means that it assumes the same constant rate of change during lifetime for all vampires. There are also models which take into account time covariates but they are beyond the scope of this short notebook. Thanks for reading and good luck with your own explorations!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# the function below is a modified version of plotting function from the lifetimes library. All credit should go to\n",
    "# them and all faults are mine.\n",
    "\n",
    "def plot_lifetimes(lifetimes, event_observed=None, birthtimes=None,\n",
    "                   order=False, block=True):\n",
    "    \"\"\"\n",
    "    Parameters:\n",
    "      lifetimes: an (n,) numpy array of lifetimes.\n",
    "      event_observed: an (n,) numpy array of booleans: True if event observed, else False.\n",
    "      birthtimes: an (n,) numpy array offsetting the births away from t=0.\n",
    "    Creates a lifetime plot, see\n",
    "    examples:\n",
    "    \"\"\"\n",
    "    from matplotlib import pyplot as plt\n",
    "\n",
    "    N = lifetimes.shape[0]\n",
    "    if N > 100:\n",
    "        print(\"warning: you may want to subsample to less than 100 individuals.\")\n",
    "\n",
    "    if event_observed is None:\n",
    "        event_observed = np.ones(N, dtype=bool)\n",
    "\n",
    "    if birthtimes is None:\n",
    "        birthtimes = np.zeros(N)\n",
    "\n",
    "    if order:\n",
    "        \"\"\"order by length of lifetimes; probably not very informative.\"\"\"\n",
    "        ix = np.argsort(lifetimes, 0)\n",
    "        lifetimes = lifetimes[ix, 0]\n",
    "        event_observed = event_observed[ix, 0]\n",
    "        birthtimes = birthtimes[ix]\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=[15,5], frameon=False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    for i in range(N):\n",
    "        c = \"#663366\" if event_observed[i] else \"green\"\n",
    "        l = 'burned by the sun rays or an angry mob' if event_observed[i] else \"alive\"\n",
    "        \n",
    "        plt.hlines(N - 1 - i, birthtimes[i], birthtimes[i] + lifetimes[i], color=c, lw=3, label=l if (i == 0) or (i==40)  else \"\")\n",
    "        m = \"|\" if not event_observed[i] else 'o'\n",
    "        plt.scatter((birthtimes[i]) + lifetimes[i], N - 1 - i, color=c, s=30, marker=m)\n",
    "        plt.legend(fontsize=16)\n",
    "\n",
    "    plt.xlabel(\"Number of years since becoming a vampire\", size=18)\n",
    "    plt.ylabel(\"Individual vampires\", size=20)\n",
    "    plt.vlines(current_time, 0, N, lw=2, linestyles='--', alpha=0.5)\n",
    "    plt.xticks(fontsize=18)\n",
    "    plt.ylim(-0.5, N)\n",
    "    return"
   ]
  }
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