{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example output from stroke outcome model\n",
    "\n",
    "In this notebook we provide an example of the output from the stroke outcome model assuming IVT is delivered at 90 mins and MT is delivered at 120 mins after stroke onset.\n",
    "\n",
    "The model provides a sample distribution of mRS scores for 1,000 patients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load packages and data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from outcome_utilities.clinical_outcome import Clinical_outcome\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Load mRS distributions\n",
    "mrs_dists = pd.read_csv(\n",
    "    './outcome_utilities/mrs_dist_probs_cumsum.csv', index_col='Stroke type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up MatPlotLib\n",
    "%matplotlib inline\n",
    "# Change default colour scheme:\n",
    "plt.style.use('seaborn-colorblind')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View the loaded mRS distributions\n",
    "For each stroke type (by row) the the imported table shows the cumulative proportion of patients with each mRS score (0-6)"
   ]
  },
  {
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stroke type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pre_stroke_nlvo</th>\n",
       "      <td>0.582881</td>\n",
       "      <td>0.745419</td>\n",
       "      <td>0.848859</td>\n",
       "      <td>0.951082</td>\n",
       "      <td>0.993055</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_stroke_nlvo_ivt_deaths</th>\n",
       "      <td>0.576469</td>\n",
       "      <td>0.737219</td>\n",
       "      <td>0.839522</td>\n",
       "      <td>0.940620</td>\n",
       "      <td>0.982131</td>\n",
       "      <td>0.989000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_stroke_lvo</th>\n",
       "      <td>0.417894</td>\n",
       "      <td>0.560853</td>\n",
       "      <td>0.679283</td>\n",
       "      <td>0.843494</td>\n",
       "      <td>0.957269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_stroke_lvo_ivt_deaths</th>\n",
       "      <td>0.403644</td>\n",
       "      <td>0.541728</td>\n",
       "      <td>0.656119</td>\n",
       "      <td>0.814731</td>\n",
       "      <td>0.924626</td>\n",
       "      <td>0.965900</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_stroke_lvo_mt_deaths</th>\n",
       "      <td>0.401178</td>\n",
       "      <td>0.538419</td>\n",
       "      <td>0.652112</td>\n",
       "      <td>0.809754</td>\n",
       "      <td>0.918978</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_treatment_nlvo</th>\n",
       "      <td>0.197144</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.580032</td>\n",
       "      <td>0.707768</td>\n",
       "      <td>0.855677</td>\n",
       "      <td>0.917702</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_effect_nlvo_ivt_deaths</th>\n",
       "      <td>0.197271</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.577583</td>\n",
       "      <td>0.702252</td>\n",
       "      <td>0.845244</td>\n",
       "      <td>0.904454</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t0_treatment_nlvo_ivt</th>\n",
       "      <td>0.429808</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.738212</td>\n",
       "      <td>0.848427</td>\n",
       "      <td>0.929188</td>\n",
       "      <td>0.956300</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_treatment_lvo</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.129000</td>\n",
       "      <td>0.265000</td>\n",
       "      <td>0.429000</td>\n",
       "      <td>0.676000</td>\n",
       "      <td>0.811000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_effect_lvo_ivt_deaths</th>\n",
       "      <td>0.047898</td>\n",
       "      <td>0.123576</td>\n",
       "      <td>0.253858</td>\n",
       "      <td>0.410962</td>\n",
       "      <td>0.647576</td>\n",
       "      <td>0.776900</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_effect_lvo_mt_deaths</th>\n",
       "      <td>0.047534</td>\n",
       "      <td>0.122637</td>\n",
       "      <td>0.251930</td>\n",
       "      <td>0.407841</td>\n",
       "      <td>0.642658</td>\n",
       "      <td>0.771000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t0_treatment_lvo_ivt</th>\n",
       "      <td>0.112916</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.327377</td>\n",
       "      <td>0.484757</td>\n",
       "      <td>0.698212</td>\n",
       "      <td>0.811443</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t0_treatment_lvo_mt</th>\n",
       "      <td>0.312767</td>\n",
       "      <td>0.434474</td>\n",
       "      <td>0.552066</td>\n",
       "      <td>0.709276</td>\n",
       "      <td>0.849898</td>\n",
       "      <td>0.912750</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0         1         2         3         4  \\\n",
       "Stroke type                                                                    \n",
       "pre_stroke_nlvo             0.582881  0.745419  0.848859  0.951082  0.993055   \n",
       "pre_stroke_nlvo_ivt_deaths  0.576469  0.737219  0.839522  0.940620  0.982131   \n",
       "pre_stroke_lvo              0.417894  0.560853  0.679283  0.843494  0.957269   \n",
       "pre_stroke_lvo_ivt_deaths   0.403644  0.541728  0.656119  0.814731  0.924626   \n",
       "pre_stroke_lvo_mt_deaths    0.401178  0.538419  0.652112  0.809754  0.918978   \n",
       "no_treatment_nlvo           0.197144  0.460000  0.580032  0.707768  0.855677   \n",
       "no_effect_nlvo_ivt_deaths   0.197271  0.460000  0.577583  0.702252  0.845244   \n",
       "t0_treatment_nlvo_ivt       0.429808  0.630000  0.738212  0.848427  0.929188   \n",
       "no_treatment_lvo            0.050000  0.129000  0.265000  0.429000  0.676000   \n",
       "no_effect_lvo_ivt_deaths    0.047898  0.123576  0.253858  0.410962  0.647576   \n",
       "no_effect_lvo_mt_deaths     0.047534  0.122637  0.251930  0.407841  0.642658   \n",
       "t0_treatment_lvo_ivt        0.112916  0.200000  0.327377  0.484757  0.698212   \n",
       "t0_treatment_lvo_mt         0.312767  0.434474  0.552066  0.709276  0.849898   \n",
       "\n",
       "                                   5    6  \n",
       "Stroke type                                \n",
       "pre_stroke_nlvo             1.000000  1.0  \n",
       "pre_stroke_nlvo_ivt_deaths  0.989000  1.0  \n",
       "pre_stroke_lvo              1.000000  1.0  \n",
       "pre_stroke_lvo_ivt_deaths   0.965900  1.0  \n",
       "pre_stroke_lvo_mt_deaths    0.960000  1.0  \n",
       "no_treatment_nlvo           0.917702  1.0  \n",
       "no_effect_nlvo_ivt_deaths   0.904454  1.0  \n",
       "t0_treatment_nlvo_ivt       0.956300  1.0  \n",
       "no_treatment_lvo            0.811000  1.0  \n",
       "no_effect_lvo_ivt_deaths    0.776900  1.0  \n",
       "no_effect_lvo_mt_deaths     0.771000  1.0  \n",
       "t0_treatment_lvo_ivt        0.811443  1.0  \n",
       "t0_treatment_lvo_mt         0.912750  1.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrs_dists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up outcome model and get output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Set up outcome model\n",
    "outcome_model = Clinical_outcome(mrs_dists)\n",
    "\n",
    "# Get outputs\n",
    "time_to_ivt = 90\n",
    "time_to_mt = 120\n",
    "outcomes = outcome_model.calculate_outcomes(\n",
    "    time_to_ivt, time_to_mt, patients=10000, random_spacing=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show raw model output\n",
    "\n",
    "The model output is a dictionary of results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lvo_untreated_probs': array([0.05 , 0.079, 0.136, 0.164, 0.247, 0.135, 0.189]),\n",
       " 'nlvo_untreated_probs': array([0.1972, 0.2628, 0.12  , 0.1278, 0.1478, 0.0621, 0.0823]),\n",
       " 'lvo_ivt_probs': array([0.0926, 0.0865, 0.1298, 0.1581, 0.2195, 0.1171, 0.1964]),\n",
       " 'lvo_mt_probs': array([0.2076, 0.1265, 0.1371, 0.1689, 0.1694, 0.0778, 0.1127]),\n",
       " 'nlvo_ivt_probs': array([0.366 , 0.2248, 0.1128, 0.1164, 0.0941, 0.033 , 0.0529]),\n",
       " 'lvo_untreated_mean_utility': 0.33261,\n",
       " 'nlvo_untreated_mean_utility': 0.5993989999999999,\n",
       " 'lvo_ivt_mean_utility': 0.37059999999999993,\n",
       " 'lvo_mt_mean_utility': 0.526139,\n",
       " 'nlvo_ivt_mean_utility': 0.7128859999999999,\n",
       " 'lvo_ivt_added_utility': 0.03798999999999991,\n",
       " 'lvo_mt_added_utility': 0.193529,\n",
       " 'nlvo_ivt_added_utility': 0.113487,\n",
       " 'lvo_untreated_cum_probs': array([0.05 , 0.129, 0.265, 0.429, 0.676, 0.811, 1.   ]),\n",
       " 'nlvo_untreated_cum_probs': array([0.1972, 0.46  , 0.58  , 0.7078, 0.8556, 0.9177, 1.    ]),\n",
       " 'lvo_ivt_cum_probs': array([0.0926, 0.1791, 0.3089, 0.467 , 0.6865, 0.8036, 1.    ]),\n",
       " 'lvo_mt_cum_probs': array([0.2076, 0.3341, 0.4712, 0.6401, 0.8095, 0.8873, 1.    ]),\n",
       " 'nlvo_ivt_cum_probs': array([0.366 , 0.5908, 0.7036, 0.82  , 0.9141, 0.9471, 1.    ]),\n",
       " 'lvo_ivt_shift': array([ 0.0426,  0.0075, -0.0062, -0.0059, -0.0275, -0.0179,  0.0074]),\n",
       " 'lvo_mt_shift': array([ 0.1576,  0.0475,  0.0011,  0.0049, -0.0776, -0.0572, -0.0763]),\n",
       " 'nlvo_ivt_shift': array([ 0.1688, -0.038 , -0.0072, -0.0114, -0.0537, -0.0291, -0.0294]),\n",
       " 'lvo_untreated_mean_mRS': 3.64,\n",
       " 'nlvo_untreated_mean_mRS': 2.2817,\n",
       " 'lvo_ivt_mean_mRS': 3.4623,\n",
       " 'lvo_mt_mean_mRS': 2.6502,\n",
       " 'nlvo_ivt_mean_mRS': 1.6584,\n",
       " 'lvo_ivt_mean_shift': -0.1777,\n",
       " 'lvo_mt_mean_shift': -0.9898,\n",
       " 'nlvo_ivt_mean_shift': -0.6233,\n",
       " 'lvo_ivt_improved': 0.1851,\n",
       " 'lvo_mt_improved': 0.7999,\n",
       " 'nlvo_ivt_improved': 0.6125}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcomes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot mRS distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mRS distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "\n",
    "# nLVO\n",
    "x = np.arange(7)\n",
    "width = 0.4\n",
    "ax1 = fig.add_subplot(121)\n",
    "y = outcomes['nlvo_untreated_probs']\n",
    "ax1.bar(x - width/2, y, width = width, label='Untreated', color='k')\n",
    "y = outcomes['nlvo_ivt_probs']\n",
    "ax1.bar(x + width/2, y, width = width, label='IVT', color='y')\n",
    "title = f'nLVO\\nTime to IVT {time_to_ivt} mins.'\n",
    "ax1.set_title(title)\n",
    "ax1.set_xlabel('mRS')\n",
    "ax1.set_ylabel('Probability')\n",
    "ax1.grid()\n",
    "ax1.legend()\n",
    "\n",
    "# LVO\n",
    "width = 0.25\n",
    "x = np.arange(7)\n",
    "ax2 = fig.add_subplot(122)\n",
    "y = outcomes['lvo_untreated_probs']\n",
    "ax2.bar(x - width, y, width = width, label='Untreated', color='k')\n",
    "y = outcomes['lvo_ivt_probs']\n",
    "ax2.bar(x, y, width = width, label='IVT', color='y')\n",
    "y = outcomes['lvo_mt_probs']\n",
    "ax2.bar(x + width, y, width = width, label='MT', color='r')\n",
    "title = f'LVO\\nTime to IVT {time_to_ivt} mins; Time to MT {time_to_mt} mins.'\n",
    "ax2.set_title(title)\n",
    "ax2.set_xlabel('mRS')\n",
    "ax2.set_ylabel('Probability')\n",
    "ax2.grid()\n",
    "ax2.legend()\n",
    "\n",
    "plt.tight_layout(pad=2)\n",
    "plt.savefig('./images/demo_mrs_dists.jpg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cumulative mRS distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "\n",
    "# nLVO\n",
    "x = np.arange(7)\n",
    "width = 0.4\n",
    "ax1 = fig.add_subplot(121)\n",
    "y = np.cumsum(outcomes['nlvo_untreated_probs'])\n",
    "ax1.bar(x - width/2, y, width = width, label='Untreated', color='k')\n",
    "y = np.cumsum(outcomes['nlvo_ivt_probs'])\n",
    "ax1.bar(x + width/2, y, width = width, label='IVT', color='y')\n",
    "title = f'nLVO\\nTime to IVT {time_to_ivt} mins.'\n",
    "ax1.set_title(title)\n",
    "ax1.set_xlabel('mRS')\n",
    "ax1.set_ylabel('Cumulative probability')\n",
    "ax1.grid()\n",
    "ax1.legend()\n",
    "\n",
    "# LVO\n",
    "width = 0.25\n",
    "x = np.arange(7)\n",
    "ax2 = fig.add_subplot(122)\n",
    "y = np.cumsum(outcomes['lvo_untreated_probs'])\n",
    "ax2.bar(x - width, y, width = width, label='Untreated', color='k')\n",
    "y = np.cumsum(outcomes['lvo_ivt_probs'])\n",
    "ax2.bar(x, y, width = width, label='IVT', color='y')\n",
    "y = np.cumsum(outcomes['lvo_mt_probs'])\n",
    "ax2.bar(x + width, y, width = width, label='MT', color='r')\n",
    "title = f'LVO\\nTime to IVT {time_to_ivt} mins; Time to MT {time_to_mt} mins.'\n",
    "ax2.set_title(title)\n",
    "ax2.set_xlabel('mRS')\n",
    "ax2.set_ylabel('Cumulative probability')\n",
    "ax2.grid()\n",
    "ax2.legend()\n",
    "\n",
    "plt.tight_layout(pad=2)\n",
    "plt.savefig('./images/demo_cum_mrs_dists.jpg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot changes in mRS proportions with treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "\n",
    "# nLVO\n",
    "x = np.arange(7)\n",
    "width = 0.8\n",
    "ax1 = fig.add_subplot(121)\n",
    "y = outcomes['nlvo_ivt_shift']\n",
    "ax1.bar(x, y, width = width, label='IVT', color='y')\n",
    "title = f'nLVO\\nTime to IVT {time_to_ivt} mins.'\n",
    "ax1.set_title(title)\n",
    "ax1.set_xlabel('mRS')\n",
    "ax1.set_ylabel('Change in probability')\n",
    "ax1.grid()\n",
    "ax1.legend()\n",
    "\n",
    "# LVO\n",
    "width = 0.4\n",
    "x = np.arange(7)\n",
    "ax2 = fig.add_subplot(122)\n",
    "y = outcomes['lvo_ivt_shift']\n",
    "ax2.bar(x - width/2, y, width = width, label='IVT', color='y')\n",
    "y = outcomes['lvo_mt_shift']\n",
    "ax2.bar(x + width/2, y, width = width, label='MT', color='r')\n",
    "title = f'LVO\\nTime to IVT {time_to_ivt} mins; Time to MT {time_to_mt} mins.'\n",
    "ax2.set_title(title)\n",
    "ax2.set_xlabel('mRS')\n",
    "ax2.set_ylabel('Probability')\n",
    "ax2.grid()\n",
    "ax2.legend()\n",
    "\n",
    "plt.tight_layout(pad=2)\n",
    "plt.savefig('./images/demo_mrs_shifts.jpg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other stats\n",
    "\n",
    "### Mean mRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean mRS\n",
      "--------\n",
      "LVO untreated: 3.64\n",
      "LVO IVT: 3.4623\n",
      "LVO MT: 2.6502\n",
      "nLVO untreated: 2.2817\n",
      "nLVO IVT: 1.6584\n"
     ]
    }
   ],
   "source": [
    "print('Mean mRS')\n",
    "print('--------')\n",
    "print('LVO untreated:', outcomes['lvo_untreated_mean_mRS'])\n",
    "print('LVO IVT:', outcomes['lvo_ivt_mean_mRS'])\n",
    "print('LVO MT:', outcomes['lvo_mt_mean_mRS'])\n",
    "print('nLVO untreated:', outcomes['nlvo_untreated_mean_mRS'])\n",
    "print('nLVO IVT:', outcomes['nlvo_ivt_mean_mRS'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean shift in mRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean mRS shift\n",
      "--------------\n",
      "LVO IVT: -0.1777\n",
      "LVO MT: -0.9898\n",
      "nLVO IVT: -0.6233\n"
     ]
    }
   ],
   "source": [
    "print('Mean mRS shift')\n",
    "print('--------------')\n",
    "print('LVO IVT:', outcomes['lvo_ivt_mean_shift'])\n",
    "print('LVO MT:', outcomes['lvo_mt_mean_shift'])\n",
    "print('nLVO IVT:', outcomes['nlvo_ivt_mean_shift'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The proportion of patients with improved mRS\n",
    "Assuming all patients move up the mRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion improved\n",
      "-------------------\n",
      "LVO IVT: 0.1851\n",
      "LVO MT: 0.7999\n",
      "nLVO IVT: 0.6125\n"
     ]
    }
   ],
   "source": [
    "print('Proportion improved')\n",
    "print('-------------------')\n",
    "print('LVO IVT:', outcomes['lvo_ivt_improved'])\n",
    "print('LVO MT:', outcomes['lvo_mt_improved'])\n",
    "print('nLVO IVT:', outcomes['nlvo_ivt_improved'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utility-weighted mRS outcomes\n",
    "\n",
    "In addition to mRS, we may calculate utility-weighted mRS (UW-mRS).\n",
    "\n",
    "UW-mRS incorporates both treatment effect and patient perceived quality of life as a single outcome measure for stroke trials.\n",
    "\n",
    "UW-mRS scores are based on a pooled analysis of 2,000+ patients. \n",
    "From  Wang X, Moullaali TJ, Li Q, Berge E, Robinson TG, Lindley R, et al.\n",
    "Utility-Weighted Modified Rankin Scale Scores for the Assessment of Stroke\n",
    "Outcome. Stroke. 2020 Aug 1;51(8):2411-7.\n",
    "\n",
    "| mRS Score | 0    | 1    | 2    | 3    | 4    | 5     | 6    |\n",
    "|-----------|------|------|------|------|------|-------|------|\n",
    "| Utility   | 0.97 | 0.88 | 0.74 | 0.55 | 0.20 | -0.19 | 0.00 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LVO untreated UW-mRS: 0.333\n",
      "LVO IVT UW-mRS: 0.371 (added UW-mRS: 0.038)\n",
      "LVO MT UW-mRS: 0.526 (added UW-mRS: 0.194)\n",
      "nLVO untreated UW-mRS: 0.599\n",
      "nLVO IVT UW-mRS: 0.713 (added UW-mRS: 0.113)\n"
     ]
    }
   ],
   "source": [
    "x = outcomes['lvo_untreated_mean_utility']\n",
    "print(f'LVO untreated UW-mRS: {x:0.3f}')\n",
    "\n",
    "x1 = outcomes['lvo_ivt_mean_utility']\n",
    "x2 = outcomes['lvo_ivt_added_utility']\n",
    "print(f'LVO IVT UW-mRS: {x1:0.3f} (added UW-mRS: {x2:0.3f})')\n",
    "\n",
    "x1 = outcomes['lvo_mt_mean_utility']\n",
    "x2 = outcomes['lvo_mt_added_utility']\n",
    "print(f'LVO MT UW-mRS: {x1:0.3f} (added UW-mRS: {x2:0.3f})')\n",
    "\n",
    "x = outcomes['nlvo_untreated_mean_utility']\n",
    "print(f'nLVO untreated UW-mRS: {x:0.3f}')\n",
    "\n",
    "x1 = outcomes['nlvo_ivt_mean_utility']\n",
    "x2 = outcomes['nlvo_ivt_added_utility']\n",
    "print(f'nLVO IVT UW-mRS: {x1:0.3f} (added UW-mRS: {x2:0.3f})')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An example showing how untreated and treated mRS are compared at a patient level\n",
    "\n",
    "In the example below we look at the treatment effect of 5 LVO patients treated with MT.\n",
    "\n",
    "After calculating the treated mRS distribution at the specified treatment time, we can sample random patients by sampling from a uniform 0-1 distribution and using that same sampled value for each patient compare their location on untreated and treated distributions.\n",
    "\n",
    "For illustration we use more evenly spaced patient values (rather than random), and we can see:\n",
    "\n",
    "* Patient #1 (P=0.1): mRS untreated = 1, mRS treated = 0\n",
    "* Patient #2 (P=0.3): mRS untreated = 3, mRS treated = 1\n",
    "* Patient #3 (P=0.5): mRS untreated = 4, mRS treated = 3\n",
    "* Patient #4 (P=0.7): mRS untreated = 5, mRS treated = 4\n",
    "* Patient #5 (P=0.9): mRS untreated = 6, mRS treated = 6\n",
    "\n",
    "This model is likely a simplification of actual effects, but should capture the average effect of treatment well, and provide a good guide to the proportion of patients who will move at least one mRS unit with treatment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from outcome_utilities.dist_plot import draw_horizontal_bar, \\\n",
    "    draw_connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_1 = outcomes['lvo_untreated_probs']\n",
    "bar_2 = outcomes['lvo_mt_probs']\n",
    "\n",
    "y_vals = [0, 1]\n",
    "y_labels = ['Treated (MT)', 'Untreated']\n",
    "    \n",
    "# Draw no effect distribution\n",
    "draw_horizontal_bar(bar_2, y_vals[0])\n",
    "\n",
    "# Add legend now to prevent doubling all the labels:\n",
    "plt.legend(loc='center',ncol=7, title='mRS', \n",
    "           bbox_to_anchor=[0.5,0.0,0.0,-0.5])   # Legend below axis.\n",
    "\n",
    "# Draww t=0 distribution\n",
    "draw_horizontal_bar(bar_1, y_vals[1])\n",
    "\n",
    "# Darw connecting lines\n",
    "draw_connections(bar_1, bar_2)\n",
    "\n",
    "for x in [0.1, 0.3, 0.5, 0.7, 0.9]:\n",
    "    plt.vlines(x, -0.25, 1.25, colors='y', linestyles='dashed')\n",
    "    \n",
    "# Add general content\n",
    "plt.xlabel('Probability')\n",
    "plt.title('Treatment effect in 5 LVO patients (dashed lines)')\n",
    "plt.xlim(0,1)\n",
    "plt.yticks(y_vals, y_labels)\n",
    "plt.savefig(f'./images/treatment_shift.jpg', dpi=300, bbox_inches='tight', \n",
    "    pad_inches=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An example of how added utility is calculated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gather distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Probability distributions:\n",
    "mRS_dist1 = outcomes['lvo_ivt_probs']\n",
    "mRS_dist2 = outcomes['lvo_untreated_probs'] \n",
    "\n",
    "# Cumulative probability distributions:\n",
    "mRS_dist1_cum = outcomes['lvo_ivt_cum_probs']\n",
    "mRS_dist2_cum = outcomes['lvo_untreated_cum_probs']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to find the added utility along the distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from outcome_utilities.added_utility_between_dists import \\\n",
    "    find_added_utility_between_dists, find_added_utility_best_worst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "(mRS_dist_mix, weighted_added_utils, x1_list, x2_list,\n",
    "    u1_list, u2_list) = find_added_utility_between_dists(mRS_dist1_cum, \n",
    "    mRS_dist2_cum, return_all=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the calculated added utilities here and from the outcome model directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0380\n",
      "0.0380\n"
     ]
    }
   ],
   "source": [
    "print(f\"{weighted_added_utils[-1]:1.4f}\")\n",
    "print(f\"{outcomes['lvo_ivt_added_utility']:1.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These numbers might not be identical to high precision because this calculation uses the full distribution whereas the outcome model samples a fixed number of points within the distribution. The numbers will become closer when a higher number of patients is used in the outcome model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume this proportion of patients are treated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop_treated = 0.85"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a weighted distribution where the patients who don't receive any treatment are distributed equally:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "weighted_added_utils_equal = weighted_added_utils*prop_treated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the best case, most or all of the patients who do not receive treatment are those who _would not_ have seen an improvement in mRS even if treated. If necessary, treatment is first withheld from the patients who would have seen the smallest increase in utility. \n",
    "\n",
    "In the worst case, most or all of the patients who do not receive treatment are those who _would_ have seen an improvement in mRS if treated. Treatment is first withheld from the patients who would have seen the largest increase in utility.\n",
    "\n",
    "Make the best- and worst-case scenarios:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mRS_diff_mix = np.diff(mRS_dist_mix, prepend=0.0)\n",
    "\n",
    "weighted_added_utils_best, weighted_added_utils_worst = (\n",
    "    find_added_utility_best_worst(prop_treated, mRS_diff_mix,\n",
    "    x1_list, x2_list, u1_list, u2_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the cumulative added utility as each mRS bin is taken into account:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, sharex=True, figsize=(8,8))\n",
    "\n",
    "y_vals = [0,1]\n",
    "y_labels = ['Untreated', 'Treated with IVT']\n",
    "\n",
    "draw_horizontal_bar(mRS_dist2, y_vals[0], ax=axs[1])\n",
    "plt.legend(loc='center', ncol=7, bbox_to_anchor=(0.5,0.0,0.0,-0.5))\n",
    "draw_horizontal_bar(mRS_dist1, y_vals[1], ax=axs[1])\n",
    "\n",
    "axs[0].plot(mRS_dist_mix, weighted_added_utils_equal, label='Equal', \n",
    "            linestyle='-')\n",
    "axs[0].plot(mRS_dist_mix, weighted_added_utils_best, label='Best-case', \n",
    "            linestyle='--')\n",
    "axs[0].plot(mRS_dist_mix, weighted_added_utils_worst, label='Worst-case', \n",
    "            linestyle='-.')\n",
    "axs[0].set_ylabel('Weighted added utility')\n",
    "axs[0].set_title('Cumulative added utility')\n",
    "\n",
    "for ax in axs:\n",
    "    for boundary in mRS_dist_mix:\n",
    "        ax.axvline(boundary, color='silver', zorder=0)\n",
    "\n",
    "axs[1].set_xlim(0,1)\n",
    "axs[1].set_xlabel('Probability')\n",
    "axs[1].set_yticks(y_vals)\n",
    "axs[1].set_yticklabels(y_labels)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "1cbbc16b0c861749b9a95c5f128e49297cfff30c52b65168a4b24d814ebeea2e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}