Survival_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "WyyAmwkDxyyB"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from datetime import date\n",
        "from datetime import datetime"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data cleaning"
      ],
      "metadata": {
        "id": "Wr0zIDjfCJwv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"https://theta.edu.pl/wp-content/uploads/2020/01/Aids2.csv\", sep = ';')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rQ6StpvIx8iI",
        "outputId": "41e42d73-e64b-42fd-8a2f-13bb3a84ab4c"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  sex        diag       death status  age  Unnamed: 5\n",
              "0   M  1929-11-08  1930-05-03      D   35         NaN\n",
              "1   M  1930-03-12  1930-05-18      D   53         NaN\n",
              "2   M  1926-02-23  1927-05-01      D   42         NaN\n",
              "3   M  1926-03-21  1926-06-06      D   44         NaN\n",
              "4   M  1927-06-02  1928-03-03      D   39         NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9c60f7be-895f-41d2-b93e-126554dab4b6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>sex</th>\n",
              "      <th>diag</th>\n",
              "      <th>death</th>\n",
              "      <th>status</th>\n",
              "      <th>age</th>\n",
              "      <th>Unnamed: 5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>1929-11-08</td>\n",
              "      <td>1930-05-03</td>\n",
              "      <td>D</td>\n",
              "      <td>35</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>1930-03-12</td>\n",
              "      <td>1930-05-18</td>\n",
              "      <td>D</td>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>1926-02-23</td>\n",
              "      <td>1927-05-01</td>\n",
              "      <td>D</td>\n",
              "      <td>42</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>1926-03-21</td>\n",
              "      <td>1926-06-06</td>\n",
              "      <td>D</td>\n",
              "      <td>44</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>1927-06-02</td>\n",
              "      <td>1928-03-03</td>\n",
              "      <td>D</td>\n",
              "      <td>39</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c60f7be-895f-41d2-b93e-126554dab4b6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9c60f7be-895f-41d2-b93e-126554dab4b6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9c60f7be-895f-41d2-b93e-126554dab4b6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop(columns = ['Unnamed: 5'])"
      ],
      "metadata": {
        "id": "SLb0kY8_zljn"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def to_date(row):\n",
        "  row = datetime.strptime(row,'%Y-%m-%d')\n",
        "  return row\n",
        "time = lambda x: x.death - x.diag\n",
        "sex = lambda x: 1 if x.sex == 'M' else 0\n",
        "status = lambda x: 1 if x.status == 'D' else 0\n",
        "def time_days(row):\n",
        "  row = row.days\n",
        "  return row"
      ],
      "metadata": {
        "id": "yE1Kf59N18WO"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sex\n",
        "\n",
        "1 - M\n",
        "\n",
        "0 - F\n",
        "\n",
        "Status\n",
        "\n",
        "1 - Dead - D\n",
        "\n",
        "0 - Live - A"
      ],
      "metadata": {
        "id": "ytnuomubB7NM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.death = data.death.apply(to_date)\n",
        "data.diag = data.diag.apply(to_date)\n",
        "data['time'] = data.apply(time, axis=1)\n",
        "data['sex'] = data.apply(sex, axis=1)\n",
        "data['status'] = data.apply(status, axis=1)\n",
        "data['time'] = data['time'].apply(time_days)"
      ],
      "metadata": {
        "id": "4CIOh5rG20pc"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LzMP9a021QdU",
        "outputId": "dcea6ab6-53ec-4345-eff3-3f25e7aaf389"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sex       diag      death  status  age  time\n",
              "0    1 1929-11-08 1930-05-03       1   35   176\n",
              "1    1 1930-03-12 1930-05-18       1   53    67\n",
              "2    1 1926-02-23 1927-05-01       1   42   432\n",
              "3    1 1926-03-21 1926-06-06       1   44    77\n",
              "4    1 1927-06-02 1928-03-03       1   39   275"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2336847d-08b4-4515-9c42-abd34e29c20d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>sex</th>\n",
              "      <th>diag</th>\n",
              "      <th>death</th>\n",
              "      <th>status</th>\n",
              "      <th>age</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1929-11-08</td>\n",
              "      <td>1930-05-03</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1930-03-12</td>\n",
              "      <td>1930-05-18</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1926-02-23</td>\n",
              "      <td>1927-05-01</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1926-03-21</td>\n",
              "      <td>1926-06-06</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1927-06-02</td>\n",
              "      <td>1928-03-03</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>275</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2336847d-08b4-4515-9c42-abd34e29c20d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2336847d-08b4-4515-9c42-abd34e29c20d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2336847d-08b4-4515-9c42-abd34e29c20d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "x4FYjS5GBmu9",
        "outputId": "a6085b6d-9199-48af-ef08-19ea4c8edf37"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               sex       status          age         time\n",
              "count  2843.000000  2843.000000  2843.000000  2843.000000\n",
              "mean      0.968695     0.619416    37.409075   405.937390\n",
              "std       0.174171     0.485616    10.063263   363.926581\n",
              "min       0.000000     0.000000     0.000000     1.000000\n",
              "25%       1.000000     0.000000    30.000000   128.000000\n",
              "50%       1.000000     1.000000    37.000000   320.000000\n",
              "75%       1.000000     1.000000    43.000000   583.000000\n",
              "max       1.000000     1.000000    82.000000  2470.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c297c830-a097-47f3-972d-baf7389066d0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>sex</th>\n",
              "      <th>status</th>\n",
              "      <th>age</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2843.000000</td>\n",
              "      <td>2843.000000</td>\n",
              "      <td>2843.000000</td>\n",
              "      <td>2843.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.968695</td>\n",
              "      <td>0.619416</td>\n",
              "      <td>37.409075</td>\n",
              "      <td>405.937390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.174171</td>\n",
              "      <td>0.485616</td>\n",
              "      <td>10.063263</td>\n",
              "      <td>363.926581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>128.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>320.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>583.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>2470.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c297c830-a097-47f3-972d-baf7389066d0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c297c830-a097-47f3-972d-baf7389066d0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c297c830-a097-47f3-972d-baf7389066d0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Survival Analysis"
      ],
      "metadata": {
        "id": "ofJetmlPCPvR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install lifelines"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Duq2tcY0CVLL",
        "outputId": "96b382a6-9e2f-431b-be2a-f233bd341332"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: lifelines in /usr/local/lib/python3.8/dist-packages (0.27.4)\n",
            "Requirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from lifelines) (1.3.5)\n",
            "Requirement already satisfied: formulaic>=0.2.2 in /usr/local/lib/python3.8/dist-packages (from lifelines) (0.5.2)\n",
            "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from lifelines) (1.7.3)\n",
            "Requirement already satisfied: matplotlib>=3.0 in /usr/local/lib/python3.8/dist-packages (from lifelines) (3.2.2)\n",
            "Requirement already satisfied: autograd>=1.5 in /usr/local/lib/python3.8/dist-packages (from lifelines) (1.5)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.8/dist-packages (from lifelines) (1.21.6)\n",
            "Requirement already satisfied: autograd-gamma>=0.3 in /usr/local/lib/python3.8/dist-packages (from lifelines) (0.5.0)\n",
            "Requirement already satisfied: future>=0.15.2 in /usr/local/lib/python3.8/dist-packages (from autograd>=1.5->lifelines) (0.16.0)\n",
            "Requirement already satisfied: interface-meta>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from formulaic>=0.2.2->lifelines) (1.3.0)\n",
            "Requirement already satisfied: graphlib-backport>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from formulaic>=0.2.2->lifelines) (1.0.3)\n",
            "Requirement already satisfied: wrapt>=1.0 in /usr/local/lib/python3.8/dist-packages (from formulaic>=0.2.2->lifelines) (1.14.1)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.8/dist-packages (from formulaic>=0.2.2->lifelines) (4.4.0)\n",
            "Requirement already satisfied: astor>=0.8 in /usr/local/lib/python3.8/dist-packages (from formulaic>=0.2.2->lifelines) (0.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.0->lifelines) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.0->lifelines) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.0->lifelines) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.0->lifelines) (1.4.4)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.0.0->lifelines) (2022.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib>=3.0->lifelines) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Filtering data to get only 2 years (max 730 days) of thearpy"
      ],
      "metadata": {
        "id": "vwcmDGxsUVjq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['time2'] = data['time']\n",
        "data['time2'][data['time2'] > 730] = 730\n",
        "data['status2'] = data['status']\n",
        "data['status2'][(data['time'] > 730) & (data['status'] == 1)] = 0\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "N46ik7gpRAZY",
        "outputId": "1f26e34e-0feb-40da-9b1c-152fadf60e2c"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-82-b86e6f93436f>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['time2'][data['time2'] > 730] = 730\n",
            "<ipython-input-82-b86e6f93436f>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['status2'][(data['time'] > 730) & (data['status'] == 1)] = 0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sex       diag      death  status  age  time  time2  status2\n",
              "0       1 1929-11-08 1930-05-03       1   35   176    176        1\n",
              "1       1 1930-03-12 1930-05-18       1   53    67     67        1\n",
              "2       1 1926-02-23 1927-05-01       1   42   432    432        1\n",
              "3       1 1926-03-21 1926-06-06       1   44    77     77        1\n",
              "4       1 1927-06-02 1928-03-03       1   39   275    275        1\n",
              "...   ...        ...        ...     ...  ...   ...    ...      ...\n",
              "2838    1 1931-06-01 1931-06-30       0   46    29     29        0\n",
              "2839    0 1931-04-07 1931-06-30       0   34    84     84        0\n",
              "2840    1 1931-06-22 1931-06-30       0   49     8      8        0\n",
              "2841    1 1931-05-17 1931-06-30       0   55    44     44        0\n",
              "2842    1 1931-05-05 1931-06-30       0   37    56     56        0\n",
              "\n",
              "[2843 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ce3e9a3-69f2-4d33-9c55-a465e74439d2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>sex</th>\n",
              "      <th>diag</th>\n",
              "      <th>death</th>\n",
              "      <th>status</th>\n",
              "      <th>age</th>\n",
              "      <th>time</th>\n",
              "      <th>time2</th>\n",
              "      <th>status2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1929-11-08</td>\n",
              "      <td>1930-05-03</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1930-03-12</td>\n",
              "      <td>1930-05-18</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>67</td>\n",
              "      <td>67</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1926-02-23</td>\n",
              "      <td>1927-05-01</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>432</td>\n",
              "      <td>432</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1926-03-21</td>\n",
              "      <td>1926-06-06</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1927-06-02</td>\n",
              "      <td>1928-03-03</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>275</td>\n",
              "      <td>275</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2838</th>\n",
              "      <td>1</td>\n",
              "      <td>1931-06-01</td>\n",
              "      <td>1931-06-30</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>29</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2839</th>\n",
              "      <td>0</td>\n",
              "      <td>1931-04-07</td>\n",
              "      <td>1931-06-30</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2840</th>\n",
              "      <td>1</td>\n",
              "      <td>1931-06-22</td>\n",
              "      <td>1931-06-30</td>\n",
              "      <td>0</td>\n",
              "      <td>49</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2841</th>\n",
              "      <td>1</td>\n",
              "      <td>1931-05-17</td>\n",
              "      <td>1931-06-30</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>44</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2842</th>\n",
              "      <td>1</td>\n",
              "      <td>1931-05-05</td>\n",
              "      <td>1931-06-30</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>56</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2843 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ce3e9a3-69f2-4d33-9c55-a465e74439d2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4ce3e9a3-69f2-4d33-9c55-a465e74439d2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4ce3e9a3-69f2-4d33-9c55-a465e74439d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Kaplan-Meier for 2nd year\n"
      ],
      "metadata": {
        "id": "gydWF5GVQxWY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import lifelines\n",
        "from lifelines import KaplanMeierFitter\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "FRU07CwNC792"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "SAl2K0IU4phH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "male = (data[\"sex\"]==1)\n",
        "female = (data[\"sex\"]==0)\n",
        "\n",
        "ax = plt.subplot(111)\n",
        "kmf = KaplanMeierFitter()\n",
        "kmf.fit(data[male][\"time2\"],data[male][\"status2\"],label=\"male\")\n",
        "kmf.plot_survival_function(ax=ax)\n",
        "\n",
        "kmf.fit(data[female][\"time2\"],data[female][\"status2\"],label=\"female\")\n",
        "kmf.plot_survival_function(ax=ax)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "-BCDq2ZSxLOb",
        "outputId": "dda48322-8c9d-4933-cea6-39c0c6682e01"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logrank test"
      ],
      "metadata": {
        "id": "BhU63SR74qS8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "H0: Male and Female patients have the same survival curves\n",
        "\n",
        "H1: Male and Female patients have different survival curves"
      ],
      "metadata": {
        "id": "lgBzvwbEZR37"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from lifelines.statistics import logrank_test\n",
        "logrank = logrank_test(data[male][\"time2\"], data[female][\"time2\"], data[male][\"status2\"], data[female][\"status2\"])\n",
        "logrank.p_value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7zcJRkK5KP3",
        "outputId": "463851c3-aa0e-4559-c172-43d93d7eaf57"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5946427341149176"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "H0: Male and Female patients have the same survival curves"
      ],
      "metadata": {
        "id": "rwWBQos26LTO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Weibul distribution"
      ],
      "metadata": {
        "id": "uKo2aKS2TgZ_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from lifelines import WeibullFitter\n",
        "wb = WeibullFitter()\n",
        "ax = plt.subplot(111)\n",
        "wb.fit(data[male][\"time2\"],data[male][\"status2\"],label=\"male\")\n",
        "wb.survival_function_.plot(ax=ax)\n",
        "wb.fit(data[female][\"time2\"],data[female][\"status2\"],label=\"female\")\n",
        "wb.survival_function_.plot(ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "al6URPV77gMg",
        "outputId": "da5df7ad-b63b-4f2a-86d4-1fa12b8c23f2"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd3hUVf7H8fdJJ6RBCBASQhIIvQVCCU16E0FEpChFKRZwRd3V1bWtLu66xR92uogCSpGiqCi9B0KvgQAhCS0BkkCAkHZ+f9wBIwRSZpKZSb6v58mTmXvvnPkmkY93zj3nXKW1RgghhP1zsHYBQgghLEMCXQghyggJdCGEKCMk0IUQooyQQBdCiDLCyVpvXKVKFR0cHGyttxdCCLu0a9eui1prv/z2WS3Qg4ODiY6OttbbCyGEXVJKnb7XPulyEUKIMkICXQghyggJdCGEKCOs1ocuhBAAWVlZJCYmkpGRYe1SbIqbmxuBgYE4OzsX+jUFBrpSajbQD0jSWjfOZ78CPgL6AteB0Vrr3YWuQAhRriUmJuLp6UlwcDBGnAitNZcuXSIxMZGQkJBCv64wXS5zgN732d8HCDN9jQe+KPS7CyHKvYyMDHx9fSXM81BK4evrW+RPLQUGutZ6I3D5PocMAOZqw3bARynlX6QqhBDlmoT53YrzO7HERdEAICHP80TTtrsopcYrpaKVUtHJycnFerOj56/wwS9HkWV/hRDij0p1lIvWerrWOkJrHeHnl+9EpwJtO3GJL9afYNWh8xauTgghim79+vX069fP2mUAlgn0M0DNPM8DTdtKxIi2tahf3ZP3fjzCjcycknobIYSwO5YI9BXASGVoC6Rprc9ZoN18OTk68O6AxpxJvcGn646X1NsIIcqRuLg46tevz+jRo6lbty6PP/44q1evpn379oSFhbFjxw527NhBZGQk4eHhtGvXjpiYmLvauXbtGk899RStW7cmPDyc5cuXl+rPUZhhiwuAzkAVpVQi8DbgDKC1ngr8hDFkMRZj2OKTJVUsAIdX0HrXHAaFv8G0DSfp28SfRjW8S/QthRCl4+8/HOLw2SsWbbNhDS/efqhRgcfFxsayaNEiZs+eTatWrZg/fz6bN29mxYoVvP/++8ydO5dNmzbh5OTE6tWref3111myZMkf2pg8eTJdu3Zl9uzZpKam0rp1a7p3707FihUt+jPdS4GBrrUeVsB+DUywWEUFyUyHE2t4e/QbbIx14eWF+1g+sT2uTo6lVoIQouwJCQmhSZMmADRq1Ihu3bqhlKJJkybExcWRlpbGqFGjOH78OEopsrKy7mrj119/ZcWKFfz3v/8FjCGZ8fHxNGjQoFR+BvubKRryAABeZ7fy70GP8eScnUxZfZxXe9e3cmFCCHMV5ky6pLi6ut5+7ODgcPu5g4MD2dnZvPnmm3Tp0oWlS5cSFxdH586d72pDa82SJUuoV69eaZX9B/a3lot3APjWgVMb6VK/KkNb1WTahhPsOp1i7cqEEGVYWloaAQHGiOw5c+bke0yvXr345JNPbg+r3rNnT2mVB9hjoAOEdILTWyAni7892AB/7wq8vHAv6TezrV2ZEKKMeuWVV3jttdcIDw8nOzv/rHnzzTfJysqiadOmNGrUiDfffLNUa1TWmqATERGhi32Di0PLYNEoGPMb1GxN1MlLDJuxnQHNA/jwsWYy60wIO3LkyJFS62O2N/n9bpRSu7TWEfkdb59n6MEdje8nNwDQJtSXF7rVZemeMyzelWjFwoQQwnrsM9Ar+kL1JnBqw+1NE7vWITLUl7eWHyI26aoVixNCCOuwz0AHY7RLwg7IugGAo4NiytDmuLs4MmHeHjKyZBapEKJ8se9Az7kJCVG3N1XzcuN/jzUj5sJV3l5+yIrFCSFE6bPfQK8VCQ5Ot/vRb+lcryoTutTmu+gEFuyIt1JxQghR+uw30F09IaAlnNp4166XetSjY1gV3l5+iL0JqVYoTgghSp/9BjoY49HP7oaMtD9sdnRQfDw0HD9PV579ZhcX029aqUAhhL34+OOPadCgAY8//niJtP/OO+/cXhKgpNh5oD8AOhdOb71rV6WKLkwb0ZLL1zJ5fv4esnNyrVCgEMJefP755/z222/MmzfP2qUUm30Hes3W4FwRDq/Id3fjAG8mD2zCtpOX+OCXo6VcnBDCXjzzzDOcPHmSPn36MHny5HyXwJ0zZw4PP/wwPXr0IDg4mE8//ZQPP/yQ8PBw2rZty+XLxp06Z8yYQatWrWjWrBmDBg3i+vXrd73fiRMn6N27Ny1btqRjx44cPWqZfLK/xbnycnKFZkNgzzzoNRncK991yKMtAzmQmMqMTaeoW82TwRE182lICGETfv4rnD9g2TarN4E+/7rvIVOnTuWXX35h3bp1fPjhh/kugQtw8OBB9uzZQ0ZGBnXq1OGDDz5gz549vPjii8ydO5dJkybxyCOPMG7cOADeeOMNZs2axfPPP/+H9xs/fjxTp04lLCyMqKgonnvuOdauXWv2j2rfgQ7QahxEz4bdc6HDpHwPeaNfQ2KT0/nb0oOEVKlIRPDdwS+EEHDvJXABunTpgqenJ56ennh7e/PQQw8B0KRJE/bv3w8Yof/GG2+QmppKeno6vXr1+kP76enpbN26lcGDB9/edvOmZa7z2X+gV2sItTpA9Cxo9zw43L0uurOjA58Pb8nDn2/h6a93sWxCe2pWdrdCsUKI+yrgTLo03GsJ3KioqAKX2AUYPXo0y5Yto1mzZsyZM4f169f/oZ3c3Fx8fHzYu3evxWu37z70W1qPhdR4OP7rPQ/xdndm5qgIsnJyGftVtKzMKITIl7lL4F69ehV/f3+ysrLyvcDq5eVFSEgIixYtAoz/gezbt8/8wikrgV6/H3j6w44Z9z2stp8Hnz3egtjkdCZ9u4ecXOusNCmEsF3mLoH73nvv0aZNG9q3b0/9+vnfeGfevHnMmjWLZs2a0ahRI4vde9Q+l8/Nz/oPYP37MHEXVKlz30PnbovjreWHGN8plNf7yrKdQliTLJ97b+Vj+dz8tBwNDs5GX3oBRkYGMzKyFtM3nmTutriSrkwIIUpF2Ql0z2rQsD/s+QZuFHw7urf6NaR7g6q8s+IQvx2+UAoFCiFEySpUoCuleiulYpRSsUqpv+azv5ZSao1Sar9Sar1SKtDypRZChxfh5hXY+kmBhzo5OvDxsHCaBHjz/ILdsuaLEFZkra5fW1ac30mBga6UcgQ+A/oADYFhSqmGdxz2X2Cu1rop8C7wzyJXYgnVm0DjQbD9C0hPKvBwdxcnZo5qhZ+nK2Pm7CT+0t0zuoQQJcvNzY1Lly5JqOehtebSpUu4ubkV6XUFXhRVSkUC72ite5mev2Z6w3/mOeYQ0FtrnaCMG3qmaa297teuxS+K3nIxFj5rDa3HQZ8PCvWSE8npDPpiK5XdXVjybDsqVXSxfF1CiHxlZWWRmJhIRkaGtUuxKW5ubgQGBuLs7PyH7fe7KFqYiUUBQEKe54lAmzuO2Qc8AnwEDAQ8lVK+WutLhS3eYqrUgebDjdmjkRPBp+Cp/rX9PJg5MoLhM6MYOzeaeWPb4OZ89wQlIYTlOTs7ExISYu0yygRLXRT9M/CAUmoP8ABwBrjrHnBKqfFKqWilVHRycrKF3jofD7xqfN9QuDN0gIjgykwZ0pzd8SlMnL9bVmcUQtidwgT6GSDvaW6gadttWuuzWutHtNbhwN9M2+66yqi1nq61jtBaR/j5+ZlRdgF8akLEU7B3vtEFU0h9m/jz7oDGrD6SxKtLDpArE4+EEHakMIG+EwhTSoUopVyAocAf1qtVSlVRSt1q6zVgtmXLLIaOL4NzBVj1OhThYsuItrV4sXtdluxOZPJPR+RCjRDCbhQY6FrrbGAisAo4AizUWh9SSr2rlOpvOqwzEKOUOgZUAyaXUL2F51EVOv8Vjq+CmJ+K9NI/davD6HbBzNp8is/XnyihAoUQwrLKztT//ORkwdSOkHkNJkSBS+FXWMzN1by4cC/L957l/YFNGN4mqAQLFUKIwikfU//z4+gMD/4P0uJhU9Hu5efgoPjv4GZ0rufH35Yd4KcD50qoSCGEsIyyHegAwe2h6VDY8jFcPF6klzo7OvDF4y1pGVSJF77dw4ZjJTgyRwghzFT2Ax2g53vg7A4rXy7SBVKACi6OzBrVijpVPRk/N5ptJ0p/aL0QQhRG+Qh0j6rQ/W04tcGYcFRE3u7OfDOmNUGV3Rnz1U52nb5cAkUKIYR5ykeggzEuPbQL/PomXD5Z5Jf7ergyb2wbqnq6Mnr2TvYnymJeQgjbUn4CXSkY8Klxz9FlEyD3romsBarq5cb8cW3xdndmxKwdHD57pQQKFUKI4ik/gQ7gHWgs2BW/1ViRsRhq+FRgwbi2uLs4MmJWFMcvXLVwkUIIUTzlK9ABmg2Den1hzbuQdKRYTdSs7M68sW1wcFAMnxnFqYvXLFykEEIUXfkLdKXgoY/AzQsWPQmZxVsDPdTPg/lj25CTqxk+YzunL0moCyGsq/wFOhijXh6ZDslH4ee/FLuZsGqefDOmDRlZOQyZtp04OVMXQlhR+Qx0gNpdjQW89nwD+74rdjMNa3gxf1xbMnNyGTJ9GyeT0y1YpBBCFF75DXSAzq9BUDv48UVIPlbsZhr4e7FgXFuyczRDp28nNklCXQhR+sp3oDs6waOzwNkNFo6Em8UP4nrVPVkwvi25+laoy+gXIUTpKt+BDuBVAwbNgosxsOwZyC3+nYrqVvPk2/FtUQqGTt/OMRnSKIQoRRLoALW7QM9/wJEfirwq453qVDVC3UEphk3fztHzMvlICFE6JNBvafucsSrjuslwdKVZTdX28+Db8W1xclQMnb5dlgkQQpQKCfRblIKHpkCNcPh+PFw4ZFZzoX4eLHq6HR6uTgyfEcWOU7KglxCiZEmg5+VcAYbMA1dPmPcYXDHvphZBvu4seiaSal6ujJwdJeupCyFKlAT6nbwDYPh3kJEK8x8za+QLgL93Bb57OpLQKh6M/WonvxyUOx8JIUqGBHp+/JvB4DlGt8viJyEn26zmqni4smB8W5oEePPcvN0s2ZVomTqFECIPCfR7Ceth3I/0+K+w8qUi3+noTt4VnPl6TBsia/vy8qJ9fL0tziJlCiHELRLo9xPxpLE8wO6vjNUZzVTR1YlZo1rRvUE13lx+iM/Xx1qgSCGEMBQq0JVSvZVSMUqpWKXUX/PZH6SUWqeU2qOU2q+U6mv5Uq2k65vQ8knY/CFs/cTs5tycHfniiRYMaF6Df/8Swz9+PExurnln/0IIAeBU0AFKKUfgM6AHkAjsVEqt0FofznPYG8BCrfUXSqmGwE9AcAnUW/qUMrpeMlLh1zegQiUIf8KsJp0dHfi/x5rjU8GZmZtPcTH9Jv9+tBkuTvKBSQhRfAUGOtAaiNVanwRQSn0LDADyBroGvEyPvYGzlizS6hwcYeB0yEiDFc+Dszs0fsS8Jh0U7/RvRFUvN/6zKobL17P44vEWVHQtzJ9ECCHuVphTwgAgIc/zRNO2vN4BnlBKJWKcnT+fX0NKqfFKqWilVHRysp2NyXZygSHfQM22sGQsHF5hdpNKKSZ0qcMHg5qw+Xgyw2ds51L6TQsUK4Qojyz1GX8YMEdrHQj0Bb5WSt3VttZ6utY6Qmsd4efnZ6G3LkUuFeHxhRAYYQxnNHOJgFuGtApi2ogIjp6/yuCp20i4XLy7KAkhyrfCBPoZoGae54GmbXmNARYCaK23AW5AFUsUaHNcPeHxxcZY9YWjIOYXizTbo2E15o1tw8X0mwz6YitHzsmiXkKIoilMoO8EwpRSIUopF2AocGd/QzzQDUAp1QAj0O2sT6UI3Lzgie+hemP47gljlUYLiAiuzOJn2+GgFI9N20bUyUsWaVcIUT4UGOha62xgIrAKOIIxmuWQUupdpVR/02EvA+OUUvuABcBorc2ciWPrKvjAiGXGYl4LR8HBJRZptm41T5Y8146qnq6MmLWDH/aVrevLQoiSo6yVuxERETo6Otoq721RN6/C/CEQvw0GfAbNh1uk2ZRrmYz/OpqdcSm82rs+zzwQilLKIm0LIeyXUmqX1joiv30y8Nlct/rUQzrBsmchappFmq1U0YWvx7ThoWY1+OCXo7y+9CDZOcW/m5IQouyTQLcEF3cY9h3U7wc/vwLr3jd77RcwZpV+NKQ5E7rUZsGOeJ76KpqrGVkWKFgIURZJoFuKsxsM/sqYRbrhA1j5MuTmmN2sg4PiL73q88GgJmyJvcjgqds4l3bDAgULIcoaCXRLcnSC/p9C+xcgepYxVj0rwyJND2kVxJejW5GYcoOHP9vCobNpFmlXCFF2SKBbmlLQ413oORkOL4evH4brlrn9XKe6fix+NhJHpXhs6jbWxSRZpF0hRNkggV5S2k2ER7+EM7tgdi9IOW2RZutX92LphPYEV6nImDk7+XLLKcr6CFEhROFIoJekxo8YY9XTL8DM7pBomWGa1bzcWPh0JN0bVOPvPxzm9aUHyZIRMEKUexLoJS24PYz5zbgB9ZwHLTYBqaKrE1OfaHl7BMyIWVGkXMu0SNtCCPskgV4a/OrBuLXGrNLFT8H6DywyrPHWCJgpQ5qzOz6VAZ9t4fiFqxYoWAhhjyTQS0vFKjByOTQbBuvfN4I985pFmn44PIDvxrflRlYOj3y+VS6WClFOSaCXJidXePgL6P53OLTUuFiaGm+RpsODKrF8QnuCfN0ZM2cnMzedlIulQpQzEuilTSnoMAmGL4SUeJjeGeI2W6TpGj4VWPRMJL0aVecfK4/w6pL93Mw2f3KTEMI+SKBbS92eRr+6uy981R+2fWaRfnV3Fyc+G96CP3ULY2F0IkOmbed8mmUmNwkhbJsEujVVqQNj10C9PrDqdWNm6U3zL2o6OChe6lGXqU+05PiFq/T7ZDM7TllmcpMQwnZJoFubm5dxr9Lufzdmls7oBskxFmm6d+PqLJ/YHi83J4bP2M7cbXHSry5EGSaBbgtu9auPWAbXLxn96vu+s0jTdap6smxiezrX8+Ot5Yd4ZfF+MrKkX12IskgC3ZaEPgDPbDbGqy8dDyuehyzzV1b0cnNm+ogIJnUPY9GuRB6bto2zqbJioxBljQS6rfHyh5EroMNLsHuu0QWTdNTsZh0cFJO612XGyAhOJV/joU82s13uWSpEmSKBboscnaD72/D4EmMdmOmdIfpLi4yC6dGwGssmtsfH3ZnHZ0bJeHUhyhAJdFsW1h2e3QpBbeHHSbBoFNxIMbvZ2n4eLJvQnh4NqvGPlUd49pvdXJE7IQlh9yTQbZ1nNXjie2ON9aMr4Yv2cHKD+c26OfPFEy1448EGrD5ygf6fbJabZghh5woV6Eqp3kqpGKVUrFLqr/ns/z+l1F7T1zGlVKrlSy3HHByMuyDdWrVxbn9Y9TfIvmlWs0opxnYM5ds868As3JlgoaKFEKWtwEBXSjkCnwF9gIbAMKVUw7zHaK1f1Fo311o3Bz4Bvi+JYsu9gBbw9EaIGAPbPoXpXeDcfrObjQiuzMo/daRVcGVeWbKfvyzax41MGdoohL0pzBl6ayBWa31Sa50JfAsMuM/xw4AFlihO5MOlIvT70FgL5vpFmNEVNv4HcrLNaraKhytfPdWaP3ULY/HuRAZ+voVTFy2zGqQQonQUJtADgLyfwxNN2+6ilKoFhABr77F/vFIqWikVnZycXNRaRV51e8Fz26HBQ7D2HzC7p9kzTB1NSwZ8OboVF65k8NAnm/npwDkLFSyEKGmWvig6FFistc7387rWerrWOkJrHeHn52fhty6H3CvD4C/h0dlw+SRM7QibPjT7bL1zvaqs/FNHwqp58Ny83byx7IDMLhXCDhQm0M8ANfM8DzRty89QpLul9DUeBBN2GCs4rvk7zOoOFw6b1WQNnwp8Nz6SpzuF8s32eB7+bAuxSXI3JCFsWWECfScQppQKUUq5YIT2ijsPUkrVByoB2yxboigUj6rw2Nfw6JfGTTOmdYJ175s1EsbFyYHX+jZgzpOtSL56k4c+2cLCnQkyEUkIG1VgoGuts4GJwCrgCLBQa31IKfWuUqp/nkOHAt9q+dduPUpB40eMs/VGA2HDB0Y3THyUWc12rleVn1/oSHiQD68s2c8L3+7lqkxEEsLmKGvlb0REhI6OjrbKe5cbx3+DH1+EtESIeAq6vQUVfIrdXE6uZuqGE3z42zECK1Xgk2HhNA0sfntCiKJTSu3SWkfkt09mipZlYT2MkTBtn4NdX8JnreHg98VeE8bRQTGhSx2+G9+WrOxcBn2xlZmbTpKbKx/KhLAFEuhlnasH9H7fuN2dZ3XjrkjfDIJLJ4rdZERwZX56oSNd61flHyuP8NRXO0m+at6sVSGE+STQy4sa4TB2LfT+FyTsgM8jYd0/i73euo+7C1OfaMm7Axqx9cQlek/ZyOrDFyxctBCiKCTQyxNHJ2j7LEzcaUxI2vAv+LwtxPxSrOaUUoyMDObH5ztQ1cuNsXOjeX3pAa5nmjcOXghRPBLo5ZGXPzw6C0YuB0cXWDAE5j1W7G6YutU8WTahHU93CmXBjnj6fbyZ/YmyPpsQpU0CvTwL7QzPbIGe/4DTW42z9dV/h5tFn0Dk6uTIa30bMG9sm9srN3669jg5csFUiFIjgV7eOblAu+fh+WhjxunmD+GTCNi7AHJzi9xcu9pV+OWFTvRp4s9/fz3GkGnbSLh8vQQKF0LcSQJdGDyrw8CpMHYNeAfCsmdgZjeI317kprzdnfl4aHOmDGlOzPmr9PloE0t2JcoMUyFKmAS6+KPACONGGgOnwdVzMLsXLBwFKXFFakYpxcPhAfw8qSMNa3jx8qJ9PPPNLhneKEQJkpmi4t4yr8HWT2DLR5CbDa3HQ6c/Q4VKRWomJ1czc9NJ/vfbMTxcnXhvQGMebOpfQkULUbbdb6aoBLoo2JWzsHYy7J0Hbt7Q6S/Qehw4uRapmeMXrvLnRfvYl5hGv6b+vDugMZUrupRQ0UKUTRLowjLOH4Tf3oITa8AnCLq+CY0fNe55WkjZOblM3XCCj9Ycx7uCC+8PbEzPRtVLsGghyhZZy0VYRvXGMOJ7GLEU3Hzg+3HGMr3HVxd6fRgnRwcmdg1j+YQO+Hm6Mv7rXbz03V7SrsvqjUKYSwJdFF3trjB+AwyaBTevwLxBMKdfkZbpbVjDi+UT2vOnrnVYvu8sPadsYF1MUgkWLUTZJ4EuisfBAZo8ChOjoc9/4OIx476m84fA+QOFasLFyYGXetZj2XPt8a7gzJNf7uTVxftJuyFn60IUh/ShC8vIvAZRU40RMRlp0PBh6PI6+NUr1MtvZufwf78dZ/rGE/h5uvLeAOlbFyI/clFUlJ4bKbDtM9j+BWRdhyaD4YFXwbd2oV6+PzGVVxbv5+j5qzzY1J93HmqEn2fRRtMIUZZJoIvSd+0SbJkCO2ZATiY0HWKMYS9EsGfl5DJtwwk+XhNLBRdH3urXkEdaBKCUKoXChbBtEujCetKTjG6YnTMhJ6tIwR6bdJVXlxxg1+kUOtX14/2BjQms5F4KRQthuyTQhfVdvWCcsUd/CTk3jfHrnf5cYB97bq7m6+2n+fcvR9HAK73qMSIyGEcHOVsX5ZMEurAd6UnGcgI7Zxp3S2o4ADq+DP5N7/uyxJTr/G3pQTYcS6ZFkA8fDGpKWDXPUipaCNth9sQipVRvpVSMUipWKfXXexzzmFLqsFLqkFJqvjkFizLMoyr0fA8mHYCOL8GJtTCtozHcMWHHPV8WWMmdOU+24v+GNOPkxWv0/XgT/10VQ0ZWTikWL4RtK/AMXSnlCBwDegCJwE5gmNb6cJ5jwoCFQFetdYpSqqrW+r6zROQMXQBwI9W4cLr9c7hxGWp1gI4vQu1ucI+LoBfTb/L+yiN8v+cMtXzdeW9AYzrV9SvlwoWwDnPP0FsDsVrrk1rrTOBbYMAdx4wDPtNapwAUFOZC3FbBBx74C7x4EHr9Ey6fhG8GGUsKHFgMOXffn7SKhysfDmnO/LFtcFSKkbN38PyCPSRdzbDCDyCE7ShMoAcACXmeJ5q25VUXqKuU2qKU2q6U6p1fQ0qp8UqpaKVUdHJycvEqFmWTS0WIfA5e2Af9PzXGsC8ZA5+0MM7gM+++61G7OlX4eVJHJnUPY9XB83T73wa+3n6aXLntnSinLDX13wkIAzoDw4AZSimfOw/SWk/XWkdorSP8/OQjssiHkwu0GAETdsKQeUaf+09/hv9rBOveh/Q/ngi4OjkyqXtdfpnUkaaB3ry57CCPfLGVw2evWOkHEMJ6ChPoZ4CaeZ4HmrbllQis0Fpnaa1PYfS5h1mmRFEuOThAg37G3ZOe/BmC2sKGD4xg/+EFSD72h8ND/Tz4ZkwbpgxpTmLKdR76dDOTVx7m2s27u2yEKKsKc1HUCSOgu2EE+U5guNb6UJ5jemNcKB2llKoC7AGaa60v3atduSgqiiz5GGz7FPZ9a4xlD+tldNOEPPCHC6hp17P41y9HWbAjnupebvztwQb0a+ovM01FmWD2OHSlVF9gCuAIzNZaT1ZKvQtEa61XKONfyv+A3kAOMFlr/e392pRAF8WWngzRs4y+9esXoVpjaPusMVnJ2e32YbtOp/D2ioMcPHOFyFBf/j6gEXVl7LqwczKxSJRNWRlwYCFsnwpJh8C9CkQ8Ba3GgKexUmNOrmbBjnj+syqG9JvZjG4XzKTuYXi6OVu5eCGKRwJdlG1aw6mNxlj2Y6vAwdFYvrfNMxAYAUpx+Vom/1kVw7c74/Gt6MrrfeszMFwW/BL2RwJdlB+XT8KOmbDna+NuSjXCofV4aPQIOLuxLyGVt1YcYl9CKhG1KvHugMY0rOFl7aqFKDQJdFH+3EyHfQuMNWOSj0KFytBiJEQ8Ra53EIt2JfDBLzGkXs9kRNtavNSjHt7u0g0jbJ8Euii/tIa4TRA1DWJ+Mp6H9YRWY0mr0YkP18Ty9fbT+Li78GKPugxrVRMnR7kzo7BdEuhCAKQlwq6vYPdXkH4BfIKg5WiO+g/g7TXJRJ26TL1qnrzRrwEdw2Tim7BNEuhC5JWdCUd/hF1fGhdTHZzQ9fux03cAf472Ij7lJt0bVMdzMG8AABPySURBVOX1vg0I9fOwdrVC/IEEuhD3cvG4cdONffPhRgq5lULZUakfr55ozJksT0a1C+ZPXcOkf13YDAl0IQqSlQFHVsCuOXB6C9rBiUMe7fnvpTYccGnBpJ4NGNY6SPrXhdVJoAtRFMnHjH72fQvg+iUuOvox72YHon36MK5/V1l7XViVBLoQxZGdCTEr0bu/hhNrUWg25zTiQNX+dB7wJA2Cqlm7QlEOSaALYa7UBLJ3f8ONHV/hmXGOK9qdg5V7ENbrafzqtbvn3ZWEsDQJdCEsJTeX9Jh1xK2eTp2La3BTWVx0C6Zi25FUaDEcvPytXaEo4yTQhSgBZy9cYNOyGYSeWU4rh2Pk4gChnXEIfxzq9QUXd2uXKMogCXQhStChs2nMWbGaoMQVDHbeQnWdjHbxRDUcAM2GGDe+dpDRMcIyJNCFKAUbjyXzr58O43lhJ2O9ttMldxtOWengFQBNBkPTIVCtobXLFHZOAl2IUpKTq1m25wz/+zWGy2lpPB9wjBHu2/FK3Ag6x7gZR5PB0ORR8A60drnCDkmgC1HKMrJymLstji/WnyDlehaD67nyl5qHqHpqBZwx/Xdfq70R7A0fBvfKVq1X2A8JdCGs5GpGFrM2n2LmplNcz8xmYHggf45wwj9hJexfCJeOg4MT1O4GjQdB/b7gKrfJE/cmgS6ElV2+lsnUDSf4amscuVozrHUQEzvXpuq1GDi4BA5+D1cSwcnNWN638SDju4yUEXeQQBfCRpxPy+CTtcf5bmcCTo6K0e1CeOaBUHzcnCAhygj3w8vgWjI4V4R6faDxI8YZfJ4bYIvySwJdCBtz+tI1pqw+zrK9Z/BwcWJ8p1BGtw82bl6dkw2nNxtn7Ud+gBuXwcXT6I5p+DDU7irhXo6ZHehKqd7AR4AjMFNr/a879o8G/gOcMW36VGs9835tSqALATHnr/K/X2P49fAFfNydGdcxlJGRtYxgB8jJMtZsP7TUCPeMVCPc6/WBhgOgTjdwrmDdH0KUKrMCXSnlCBwDegCJwE5gmNb6cJ5jRgMRWuuJhS1KAl2I3x1ITGPK6mOsOZp0O9hHtQvGw9Xp94NysuDkBqNL5uiPcCPF6Jap2wsa9oc6PcBVbshR1pkb6JHAO1rrXqbnrwForf+Z55jRSKALYbb9ial8tPr4/YMdjHCP2wSHVxhn7tcvGhdUa3czwr1uL6hQyTo/hChR5gb6o0BvrfVY0/MRQJu84W0K9H8CyRhn8y9qrRPyaWs8MB4gKCio5enTp4v1AwlR1u1LSOWjNcdZW1CwA+TmQPy238P96lljKGRwR2jQD+r3A8/qpf9DiBJRGoHuC6RrrW8qpZ4Ghmitu96vXTlDF6JgRQp2gNxcOLvbCPYjP8DlE8b2wFZQ/0Ej3KuEld4PICyuxLtc7jjeEbistfa+X7sS6EIU3t6EVD5afYx1MclUcndmbMdQRkTWwsvtPvc61RqSj8KRH40+93N7je1V6hqrQdbvBwEtZeEwO2NuoDthdKN0wxjFshMYrrU+lOcYf631OdPjgcCrWuu292tXAl2IotubkMqU1cdYH5OMp5sToyKDeapDCJUruhT84tQEiPkJjq6E01sgNxsqVoV6vY2AD3lAJjLZAUsMW+wLTMEYtjhbaz1ZKfUuEK21XqGU+ifQH8gGLgPPaq2P3q9NCXQhiu/gmTQ+WxfLL4fO4+bkyPA2QYzrGEp170KOT7+RAsdXQ8xK43vmVXCqAKGdjYCv21v63W2UTCwSooyKTbrK5+tPsHzvWRyVYlDLQJ59oDZBvkU4087ONCYyxfwMMb9AWryxvUa4Eex1e0H1ZtI1YyMk0IUo4xIuX2faxhMsjE4kJ1fTv1kNnu1cm7rVirjQl9aQdNgI92OrIHEnoMGjOoT1MMI9tLMsIGZFEuhClBNJVzKYsekk86LiuZ6ZQ69G1ZjYJYwmgfcdo3Bv1y7C8d/g2M9wYh3cvAIOzhDc3lg8LKwn+NaRm2SXIgl0IcqZlGuZfLk1jjlbTnElI5v2dXx5ulNtOoZVQRU3fHOyIH47HF9lhHyy6TJZpWBjlmpYTwjuIBdWS5gEuhDl1NWMLOZFxfPlllNcuHKTBv5ePN0plAeb+uPsaGafeMppiP3NCPdTGyHrOji6GqFep7vRRSNn7xYngS5EOXczO4fle88yY+NJjielE+BTgac6hDC0VU0q3muSUlFkZRhDIWNXG18XjxnbfYKM5QjqdDOGRbp5mf9e5ZwEuhACgNxczfpjSUzbcJKoU5fxcnNiRGQtRrULpqqnBZfkTTkNJ9YYQyJPbTSGRSpHqNnGWP63dleo0RwcHC33nuWEBLoQ4i57E1KZvvEEPx88j7ODA4+0CGBcp1Bq+1l4xcacLEjYYZy5n1j7+4zVCpWMs/ZbAe9T07LvW0ZJoAsh7inu4jVmbj7JouhEMnNy6d6gGmM6hNAmpHLxL6Dez7WLcHI9xK6Bk+vg6jlju28dY0hkaBejH76Cj+XfuwyQQBdCFOhi+k3mbjvNN9tPc/laJg39vXiqQwgPNfPH1amEuka0huQY48z95HqI2wxZ10A5GOvMhHY2vgJbgZNrydRgZyTQhRCFlpGVw7I9Z5i95RTHLqRTxcOVEW1r8XjbIKp4lHCoZmcak5lOrjNu5nFmF+gcY1mCWpEQ0snopvFvVm773yXQhRBFprVmS+wlZm0+ybqYZFwcHRjQvAZPtg+hYY1SGq2SkQZxW+DUBiPgk48Y2928jfXeQzoZX371y83wSAl0IYRZTiSnM2dLHIt3JXIjK4fIUF/GdAiha/2qODiUYpBevWCMmjm1wfiearpJTkW/PwZ85dAyG/AS6EIIi0i7nsWCnfF8tTWOc2kZBPu6M7pdMINaBv5+Y+vSlBIHpzYZt+M7tfH3C6yeNYwLqyEdje+VQspMwEugCyEsKisnl1WHzjNr8yn2xKdS0cWRgS0CGBkZXPQFwSxFa7h0AuI2mkJ+M1xLMvZ5BUCt9sYaNLU6gG9tuw14CXQhRInZl5DK3G2n+WH/WTKzc2kbWpmRkcH0aFjN/OUFzKE1XDxunL3HbTZmsqZfMPZ5VIda7UwB396u+uAl0IUQJe7ytUy+25nAN9tPcyb1BtW93BjeJoihrWtadhZqcWkNl2J/D/e4LcYNtQHcfSEo0gj5Wu2gWhNwtMCSCCVAAl0IUWpycjVrjyYxd1scm45fxNlR0aexPyMja9GyVqWSmaxUHFpDyikj2OO3GSGfEmfsc/GAmq2NcA9qBwEtwLmCVcu9RQJdCGEVJ5PT+Xr7aRbvSuRqRjYN/L0YGVmLAc1r4O5ig2fAaWdM4b7V+J502Nju6GLcwSmorXEmX7MNuFe2SokS6EIIq7p2M5tle8/w9bbTHD1/FU9XJx4OD2BY66DSG9NeHNcvG+vQxG811oI/sxtys4x9VepBUJvfA76UhkpKoAshbILWmujTKcyPimflgXNkZufSvKYPw1sH0a+Zv22eteeVdQPO7jHO3uO3Q0KUMfkJoGJVo5umZhvjTN6/WYksVyCBLoSwOanXM1my+wzzo05zIvna7bP24W2CaOBvw2fteeXmwsUYU8BHGQGfcsrYd6ubJrCVEfI1W4NndbPf0uxAV0r1Bj4CHIGZWut/3eO4QcBioJXW+r5pLYEuhADjrH1nXArzo07z08HzZGbnEh7kw7DWQfRragdn7Xe6esEI9sQdRnfN2T2Qk2ns8wmCwNYQ8ZQxZLIYzAp0pZQjcAzoASQCO4FhWuvDdxznCawEXICJEuhCiKJKuZbJkt2JLNgRf/usfWALo6/dbs7a75R9E87tM8L9Vsj3/Ac0ebRYzZkb6JHAO1rrXqbnrwForf95x3FTgN+AvwB/lkAXQhRXfmftTQO9GRxRk/7NauBdwQrLDFiK1saXQ/EmXd0v0AvTYgCQkOd5omlb3jdoAdTUWq8soJDxSqlopVR0cnJyId5aCFEeKaVoHVKZKUPDiXqtG2/2a0hmdi5vLjtI68mreeHbPWyJvUhurnWuAZpFqWKHeUHM7pxSSjkAHwKjCzpWaz0dmA7GGbq57y2EKPsqVXRhTIcQnmofzMEzV1gYncDyvWdYvvcsAT4VGBwRyKMtAwms5G7tUq3O7C4XpZQ3cAJIN72kOnAZ6H+/bhfpchFCFFdGVg6rDp1n8a5ENsdeBKB97SoMjgikV6PquDmX3ZtfmNuH7oRxUbQbcAbjouhwrfWhexy/HulDF0KUksSU6yzZdYZFuxJITLmBl5sT/ZvXYHDLmjQN9LadpQYs5H6BXmCXi9Y6Wyk1EViFMWxxttb6kFLqXSBaa73CsuUKIUThBVZy54XuYTzftQ7bT15iYXQCi6IT+WZ7PLX9KvJIi0AeDg8gwMc21mIpSTKxSAhR5qTdyOKnA+f4fnciO+NSUArahvgysEUAfRpXt87NOCxEZooKIcqt+EvXWbrnDEv3JBJ36Tpuzg70bFidgS0C6FinCk7WXLO9GCTQhRDlntaaPQmpfL87kR/3nyP1ehZVPFwZ0LwGA8MDaFTDyy762yXQhRAij8zsXNbFJPH97kTWHk0iK0dTt5oHj7QIpH+zGtSw4f52CXQhhLiHlGuZ/HjgHEt3J7I7PhWAVsGV6N+sBn2b+OPrYfkVE80hgS6EEIUQd/EaP+w7y4p9ZzmelI6jg6J9nSr0b1aDXo2q2cTFVAl0IYQoAq01R89fZcW+s/yw7yyJKTdwcXKga72q9G9eg671q1pt8pIEuhBCFJPWmt3xqfyw7yw/7j/HxfSbeLg60bNhNR5qXoMOdargXIojZSTQhRDCAnJyNdtPXmLF3rP8fPAcVzKyqVzRhT6Nq9OvaQ1ah1TG0aFkR8pIoAshhIXdzM5h47GLrNh3ltWHL3AjK4cqHq70blyNvk38aRPiWyLhLoEuhBAl6HpmNutjklm5/xxrjyaZwt2FXo2q82ATf1qHVLbYBCYJdCGEKCU3MnNYF5PEygPnWHvECHffii70amyEexszw10CXQghrOBGZg7rb4X70SSuZxrh/tZDDRnQPKDgBvJh1mqLQgghiqeCiyN9mvjTp4k/NzJz2HAsiZUHzuPvXTIzUSXQhRCiFFRwcaR3Y396N/Yvsfewr2XGhBBC3JMEuhBClBES6EIIUUZIoAshRBkhgS6EEGWEBLoQQpQREuhCCFFGSKALIUQZYbWp/0qpZOB0MV9eBbhowXJKij3UaQ81gtRpafZQpz3UCKVfZy2ttV9+O6wW6OZQSkXfay0DW2IPddpDjSB1Wpo91GkPNYJt1SldLkIIUUZIoAshRBlhr4E+3doFFJI91GkPNYLUaWn2UKc91Ag2VKdd9qELIYS4m72eoQshhLiDBLoQQpQRdhXoSqneSqkYpVSsUuqvVq5ltlIqSSl1MM+2ykqp35RSx03fK5m2K6XUx6a69yulWpRinTWVUuuUUoeVUoeUUi/YWq1KKTel1A6l1D5TjX83bQ9RSkWZavlOKeVi2u5qeh5r2h9c0jXeUa+jUmqPUupHW61TKRWnlDqglNqrlIo2bbOZv3meOn2UUouVUkeVUkeUUpG2VqdSqp7p93jr64pSapKt1QmA1touvgBH4AQQCrgA+4CGVqynE9ACOJhn27+Bv5oe/xX4wPS4L/AzoIC2QFQp1ukPtDA99gSOAQ1tqVbTe3mYHjsDUab3XggMNW2fCjxrevwcMNX0eCjwXSn/7V8C5gM/mp7bXJ1AHFDljm028zfPU9NXwFjTYxfAxxbrzFOvI3AeqGWLdZbqL8PMX2QksCrP89eA16xcU/AdgR4D+Jse+wMxpsfTgGH5HWeFmpcDPWy1VsAd2A20wZh953Tn3x9YBUSaHjuZjlOlVF8gsAboCvxo+kdri3XmF+g29TcHvIFTd/5ObK3OO2rrCWyx1TrtqcslAEjI8zzRtM2WVNNanzM9Pg9UMz22idpNH/nDMc6AbapWUzfGXiAJ+A3j01iq1jo7nzpu12janwb4lnSNJlOAV4Bc03NfG61TA78qpXYppcabttnU3xwIAZKBL01dWDOVUhVtsM68hgILTI9trk57CnS7oo3/NdvMmFCllAewBJiktb6Sd58t1Kq1ztFaN8c4A24N1LdmPflRSvUDkrTWu6xdSyF00Fq3APoAE5RSnfLutIW/OcanlhbAF1rrcOAaRtfFbTZSJwCmayP9gUV37rOVOu0p0M8ANfM8DzRtsyUXlFL+AKbvSabtVq1dKeWMEebztNbf23KtWutUYB1G14WPUsopnzpu12ja7w1cKoXy2gP9lVJxwLcY3S4f2WCdaK3PmL4nAUsx/idpa3/zRCBRax1ler4YI+Btrc5b+gC7tdYXTM9trk57CvSdQJhpRIELxkefFVau6U4rgFGmx6Mw+qtvbR9puvrdFkjL81GtRCmlFDALOKK1/tAWa1VK+SmlfEyPK2D08R/BCPZH71HjrdofBdaazpBKlNb6Na11oNY6GOO/v7Va68dtrU6lVEWllOetxxj9vgexob85gNb6PJCglKpn2tQNOGxrdeYxjN+7W27VY1t1luYFBQtckOiLMUrjBPA3K9eyADgHZGGcaYzB6B9dAxwHVgOVTccq4DNT3QeAiFKsswPGR8H9wF7TV19bqhVoCuwx1XgQeMu0PRTYAcRifMx1NW13Mz2PNe0PtcLfvzO/j3KxqTpN9ewzfR269W/Flv7meWptDkSb/vbLgEo2WmdFjE9X3nm22VydMvVfCCHKCHvqchFCCHEfEuhCCFFGSKALIUQZIYEuhBBlhAS6EEKUERLoQghRRkigCyFEGfH/K1hZJpEI1DkAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(wb.lambda_, wb.rho_)\n",
        "print(wb.predict(20))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "puzjN7O4GMsm",
        "outputId": "1b95d9bc-d2fa-467b-e1d1-7a8d120bef79"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "870.5870888639881 0.6142470950650616\n",
            "0.906206678072434\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Comparison"
      ],
      "metadata": {
        "id": "FamVgThLQrZp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from lifelines.plotting import qq_plot \n",
        "from lifelines import WeibullFitter, ExponentialFitter, LogNormalFitter\n",
        "for model in [WeibullFitter(),\n",
        "              LogNormalFitter(),\n",
        "              ExponentialFitter()]:\n",
        "  model.fit(data[\"time2\"],data[\"status2\"])\n",
        "  model.plot_survival_function(ax = ax)\n",
        "  qq_plot(model)\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "id": "w9LmDbmWG0DT",
        "outputId": "5181203c-5a55-45e6-d66e-994537f00292"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The best qqplot has the Exponential distribution"
      ],
      "metadata": {
        "id": "0iUqhfCCbZhw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wb = WeibullFitter().fit(data[\"time\"],data[\"status\"])\n",
        "exp = ExponentialFitter().fit(data[\"time\"],data[\"status\"])\n",
        "log_n = LogNormalFitter().fit(data[\"time\"],data[\"status\"])\n",
        "\n",
        "print(wb.AIC_, exp.AIC_, log_n.AIC_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvA115TVItcx",
        "outputId": "e8b17ecf-f442-43b6-9964-9286897431c9"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26334.99075850715 26364.793180007837 26816.853987007118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The best AIC has the Weibull distribution"
      ],
      "metadata": {
        "id": "4aUKnaY-bYvK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cox"
      ],
      "metadata": {
        "id": "0VmN6HAUQmFu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data2 = data.drop(columns = ['diag', 'death', 'time', 'status'])\n",
        "data1 = data.drop(columns = ['diag', 'death', 'time2', 'status2'])"
      ],
      "metadata": {
        "id": "XVmtX8aCU6BL"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from lifelines import CoxPHFitter\n",
        "coxph = CoxPHFitter()\n",
        "coxph.fit(df=data2, duration_col=\"time2\", event_col=\"status2\")\n",
        "coxph.summary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "c7GWEL61JL41",
        "outputId": "17c24818-e3d4-434e-b5dd-c1539ae1ec33"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               coef  exp(coef)  se(coef)  coef lower 95%  coef upper 95%  \\\n",
              "covariate                                                                  \n",
              "sex        0.060917   1.062810  0.149712       -0.232513        0.354346   \n",
              "age        0.017569   1.017724  0.002617        0.012439        0.022698   \n",
              "\n",
              "           exp(coef) lower 95%  exp(coef) upper 95%  cmp to         z  \\\n",
              "covariate                                                               \n",
              "sex                   0.792540             1.425248     0.0  0.406892   \n",
              "age                   1.012517             1.022957     0.0  6.713316   \n",
              "\n",
              "                      p   -log2(p)  \n",
              "covariate                           \n",
              "sex        6.840870e-01   0.547748  \n",
              "age        1.902502e-11  35.613311  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9d7c9888-d2c1-42ea-8c7d-d62fe7124a35\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>coef</th>\n",
              "      <th>exp(coef)</th>\n",
              "      <th>se(coef)</th>\n",
              "      <th>coef lower 95%</th>\n",
              "      <th>coef upper 95%</th>\n",
              "      <th>exp(coef) lower 95%</th>\n",
              "      <th>exp(coef) upper 95%</th>\n",
              "      <th>cmp to</th>\n",
              "      <th>z</th>\n",
              "      <th>p</th>\n",
              "      <th>-log2(p)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>covariate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>sex</th>\n",
              "      <td>0.060917</td>\n",
              "      <td>1.062810</td>\n",
              "      <td>0.149712</td>\n",
              "      <td>-0.232513</td>\n",
              "      <td>0.354346</td>\n",
              "      <td>0.792540</td>\n",
              "      <td>1.425248</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.406892</td>\n",
              "      <td>6.840870e-01</td>\n",
              "      <td>0.547748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.017569</td>\n",
              "      <td>1.017724</td>\n",
              "      <td>0.002617</td>\n",
              "      <td>0.012439</td>\n",
              "      <td>0.022698</td>\n",
              "      <td>1.012517</td>\n",
              "      <td>1.022957</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.713316</td>\n",
              "      <td>1.902502e-11</td>\n",
              "      <td>35.613311</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d7c9888-d2c1-42ea-8c7d-d62fe7124a35')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9d7c9888-d2c1-42ea-8c7d-d62fe7124a35 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9d7c9888-d2c1-42ea-8c7d-d62fe7124a35');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coxph.check_assumptions(data2, p_value_threshold=0.05)\n",
        "#coxph.predict_median(X, conditional_after)\n",
        "#coxph.predict_survival_function(X, conditional_after)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "id": "AIJxESYcRFKr",
        "outputId": "1f6a7abd-3a7d-4d02-837a-a101d6325992"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The ``p_value_threshold`` is set at 0.05. Even under the null hypothesis of no violations, some\n",
            "covariates will be below the threshold by chance. This is compounded when there are many covariates.\n",
            "Similarly, when there are lots of observations, even minor deviances from the proportional hazard\n",
            "assumption will be flagged.\n",
            "\n",
            "With that in mind, it's best to use a combination of statistical tests and visual tests to determine\n",
            "the most serious violations. Produce visual plots using ``check_assumptions(..., show_plots=True)``\n",
            "and looking for non-constant lines. See link [A] below for a full example.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<lifelines.StatisticalResult: proportional_hazard_test>\n",
              " null_distribution = chi squared\n",
              "degrees_of_freedom = 1\n",
              "             model = <lifelines.CoxPHFitter: fitted with 2843 total observations, 1294 right-censored observations>\n",
              "         test_name = proportional_hazard_test\n",
              "\n",
              "---\n",
              "          test_statistic      p  -log2(p)\n",
              "age km             18.37 <0.005     15.75\n",
              "    rank           17.13 <0.005     14.80\n",
              "sex km              5.43   0.02      5.66\n",
              "    rank            5.86   0.02      6.01"
            ],
            "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",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>null_distribution</th>\n",
              "      <td>chi squared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>degrees_of_freedom</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <td>&lt;lifelines.CoxPHFitter: fitted with 2843 total...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test_name</th>\n",
              "      <td>proportional_hazard_test</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>test_statistic</th>\n",
              "      <th>p</th>\n",
              "      <th>-log2(p)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">age</th>\n",
              "      <th>km</th>\n",
              "      <td>18.37</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>15.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rank</th>\n",
              "      <td>17.13</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>14.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">sex</th>\n",
              "      <th>km</th>\n",
              "      <td>5.43</td>\n",
              "      <td>0.02</td>\n",
              "      <td>5.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rank</th>\n",
              "      <td>5.86</td>\n",
              "      <td>0.02</td>\n",
              "      <td>6.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/latex": "\\begin{tabular}{llrrr}\n{} & {} & {test_statistic} & {p} & {-log2(p)} \\\\\n\\multirow[c]{2}{*}{age} & km & 18.37 & 0.00 & 15.75 \\\\\n & rank & 17.13 & 0.00 & 14.80 \\\\\n\\multirow[c]{2}{*}{sex} & km & 5.43 & 0.02 & 5.66 \\\\\n & rank & 5.86 & 0.02 & 6.01 \\\\\n\\end{tabular}\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "1. Variable 'sex' failed the non-proportional test: p-value is 0.0155.\n",
            "\n",
            "   Advice: with so few unique values (only 2), you can include `strata=['sex', ...]` in the call in\n",
            "`.fit`. See documentation in link [E] below.\n",
            "\n",
            "2. Variable 'age' failed the non-proportional test: p-value is <5e-05.\n",
            "\n",
            "   Advice 1: the functional form of the variable 'age' might be incorrect. That is, there may be\n",
            "non-linear terms missing. The proportional hazard test used is very sensitive to incorrect\n",
            "functional forms. See documentation in link [D] below on how to specify a functional form.\n",
            "\n",
            "   Advice 2: try binning the variable 'age' using pd.cut, and then specify it in `strata=['age',\n",
            "...]` in the call in `.fit`. See documentation in link [B] below.\n",
            "\n",
            "   Advice 3: try adding an interaction term with your time variable. See documentation in link [C]\n",
            "below.\n",
            "\n",
            "\n",
            "---\n",
            "[A]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html\n",
            "[B]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Bin-variable-and-stratify-on-it\n",
            "[C]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Introduce-time-varying-covariates\n",
            "[D]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Modify-the-functional-form\n",
            "[E]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Stratification\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coxph = CoxPHFitter()\n",
        "coxph.fit(df=data1, duration_col=\"time\", event_col=\"status\")\n",
        "coxph.summary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "SJ45vY3Ut0Z7",
        "outputId": "05a63029-93ba-46c7-dfb3-b4e9a64f9c9a"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               coef  exp(coef)  se(coef)  coef lower 95%  coef upper 95%  \\\n",
              "covariate                                                                  \n",
              "sex        0.104277   1.109908  0.139598       -0.169329        0.377884   \n",
              "age        0.015087   1.015201  0.002457        0.010272        0.019902   \n",
              "\n",
              "           exp(coef) lower 95%  exp(coef) upper 95%  cmp to         z  \\\n",
              "covariate                                                               \n",
              "sex                   0.844231             1.459194     0.0  0.746984   \n",
              "age                   1.010325             1.020102     0.0  6.140792   \n",
              "\n",
              "                      p   -log2(p)  \n",
              "covariate                           \n",
              "sex        4.550734e-01   1.135829  \n",
              "age        8.211122e-10  30.181702  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3631c9cf-6ab7-43db-8550-1e3fb1cb200f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>coef</th>\n",
              "      <th>exp(coef)</th>\n",
              "      <th>se(coef)</th>\n",
              "      <th>coef lower 95%</th>\n",
              "      <th>coef upper 95%</th>\n",
              "      <th>exp(coef) lower 95%</th>\n",
              "      <th>exp(coef) upper 95%</th>\n",
              "      <th>cmp to</th>\n",
              "      <th>z</th>\n",
              "      <th>p</th>\n",
              "      <th>-log2(p)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>covariate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>sex</th>\n",
              "      <td>0.104277</td>\n",
              "      <td>1.109908</td>\n",
              "      <td>0.139598</td>\n",
              "      <td>-0.169329</td>\n",
              "      <td>0.377884</td>\n",
              "      <td>0.844231</td>\n",
              "      <td>1.459194</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.746984</td>\n",
              "      <td>4.550734e-01</td>\n",
              "      <td>1.135829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.015087</td>\n",
              "      <td>1.015201</td>\n",
              "      <td>0.002457</td>\n",
              "      <td>0.010272</td>\n",
              "      <td>0.019902</td>\n",
              "      <td>1.010325</td>\n",
              "      <td>1.020102</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.140792</td>\n",
              "      <td>8.211122e-10</td>\n",
              "      <td>30.181702</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3631c9cf-6ab7-43db-8550-1e3fb1cb200f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3631c9cf-6ab7-43db-8550-1e3fb1cb200f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3631c9cf-6ab7-43db-8550-1e3fb1cb200f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coxph.check_assumptions(data1, p_value_threshold=0.05)\n",
        "#coxph.predict_median(X, conditional_after)\n",
        "#coxph.predict_survival_function(X, conditional_after)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "outputId": "9df90e0d-0c3e-4ff6-c2e7-f68b8103598b",
        "id": "eaUUuCJxtZzi"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The ``p_value_threshold`` is set at 0.05. Even under the null hypothesis of no violations, some\n",
            "covariates will be below the threshold by chance. This is compounded when there are many covariates.\n",
            "Similarly, when there are lots of observations, even minor deviances from the proportional hazard\n",
            "assumption will be flagged.\n",
            "\n",
            "With that in mind, it's best to use a combination of statistical tests and visual tests to determine\n",
            "the most serious violations. Produce visual plots using ``check_assumptions(..., show_plots=True)``\n",
            "and looking for non-constant lines. See link [A] below for a full example.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<lifelines.StatisticalResult: proportional_hazard_test>\n",
              " null_distribution = chi squared\n",
              "degrees_of_freedom = 1\n",
              "             model = <lifelines.CoxPHFitter: fitted with 2843 total observations, 1082 right-censored observations>\n",
              "         test_name = proportional_hazard_test\n",
              "\n",
              "---\n",
              "          test_statistic      p  -log2(p)\n",
              "age km             25.91 <0.005     21.42\n",
              "    rank           25.21 <0.005     20.89\n",
              "sex km              5.47   0.02      5.69\n",
              "    rank            6.21   0.01      6.30"
            ],
            "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",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>null_distribution</th>\n",
              "      <td>chi squared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>degrees_of_freedom</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <td>&lt;lifelines.CoxPHFitter: fitted with 2843 total...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test_name</th>\n",
              "      <td>proportional_hazard_test</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>test_statistic</th>\n",
              "      <th>p</th>\n",
              "      <th>-log2(p)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">age</th>\n",
              "      <th>km</th>\n",
              "      <td>25.91</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>21.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rank</th>\n",
              "      <td>25.21</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>20.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">sex</th>\n",
              "      <th>km</th>\n",
              "      <td>5.47</td>\n",
              "      <td>0.02</td>\n",
              "      <td>5.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rank</th>\n",
              "      <td>6.21</td>\n",
              "      <td>0.01</td>\n",
              "      <td>6.30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/latex": "\\begin{tabular}{llrrr}\n{} & {} & {test_statistic} & {p} & {-log2(p)} \\\\\n\\multirow[c]{2}{*}{age} & km & 25.91 & 0.00 & 21.42 \\\\\n & rank & 25.21 & 0.00 & 20.89 \\\\\n\\multirow[c]{2}{*}{sex} & km & 5.47 & 0.02 & 5.69 \\\\\n & rank & 6.21 & 0.01 & 6.30 \\\\\n\\end{tabular}\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "1. Variable 'sex' failed the non-proportional test: p-value is 0.0127.\n",
            "\n",
            "   Advice: with so few unique values (only 2), you can include `strata=['sex', ...]` in the call in\n",
            "`.fit`. See documentation in link [E] below.\n",
            "\n",
            "2. Variable 'age' failed the non-proportional test: p-value is <5e-05.\n",
            "\n",
            "   Advice 1: the functional form of the variable 'age' might be incorrect. That is, there may be\n",
            "non-linear terms missing. The proportional hazard test used is very sensitive to incorrect\n",
            "functional forms. See documentation in link [D] below on how to specify a functional form.\n",
            "\n",
            "   Advice 2: try binning the variable 'age' using pd.cut, and then specify it in `strata=['age',\n",
            "...]` in the call in `.fit`. See documentation in link [B] below.\n",
            "\n",
            "   Advice 3: try adding an interaction term with your time variable. See documentation in link [C]\n",
            "below.\n",
            "\n",
            "\n",
            "---\n",
            "[A]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html\n",
            "[B]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Bin-variable-and-stratify-on-it\n",
            "[C]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Introduce-time-varying-covariates\n",
            "[D]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Modify-the-functional-form\n",
            "[E]  https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Stratification\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is no significant difference in influence of sex and age on the status of patient between 2 and 5 years"
      ],
      "metadata": {
        "id": "oOij5qcZvGe7"
      }
    }
  ]
}