survival-analysis-for-data-analysis-introduction-colab.ipynb

survival-analysis-for-data-analysis-introduction-colab.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/alonsosilvaallende/6f6a807703964b3f7f3add1cf3b05149/survival-analysis-for-data-analysis-introduction-colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aEj365e4uTvD"
      },
      "source": [
        "# Survival Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "heading_collapsed": true,
        "id": "0Z1wEfUFuTvG"
      },
      "source": [
        "* Historically, survival analysis was developed and used by actuaries\n",
        "and medical researchers to measure the lifetime of populations.\n",
        "* What's the expected lifetime of patients that were given drug A? drug B?\n",
        "* What's the life-expectancy of a baby born today in France?\n",
        "\n",
        "These researchers wanted to measure the duration between *Birth* and *Death*\n",
        "\n",
        "\n",
        "Source: [Lifelines: Survival Analysis in Python](https://www.youtube.com/watch?v=XQfxndJH4UA)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JxSbfi7QuTvI"
      },
      "source": [
        "# Survival function and hazard function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXfTmltSuTvK"
      },
      "source": [
        "**Definition:** Let $T$ be a random variable called failure time.\n",
        "\n",
        "- $f(t)$ be its probability density function\n",
        "- $F(t):=\\mathcal{P}(T\\le t)$ its cumulative distribution function\n",
        "\n",
        "Then we define\n",
        "\n",
        "- The *survival function* $S(t):=\\mathcal{P}(T>t)=1-F(t)$.\n",
        "- The *hazard function* (probability of failure between $t$ and $t+\\delta t$ knowing that it was working at time $t$):\n",
        "$$\n",
        "h(t):=\\lim_{\\delta t\\to0}\\frac{\\mathcal{P}(T<t+\\delta t|T>t)}{\\delta t}=\n",
        "\\lim_{\\delta t\\to0}\\frac{F(t+\\delta t)-F(t)}{\\delta t}\\times\\frac{1}{1-F(t)}=\\frac{f(t)}{1-F(t)}.\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBY8J3sxuTvM"
      },
      "source": [
        "**Properties:**\n",
        "- $S(t)=\\exp(-\\int_0^t h(s)\\,ds)$.\n",
        "- $h(t)=-\\frac{d}{dt}\\ln(S(t))$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VYNZWpmuTvO"
      },
      "source": [
        "# Right censoring\n",
        "\n",
        "By the end of the study, the event of interest (for example, in medicine \"death of a patient\" or \"churn of a customer\") has only occurred for a subset of the observations.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vao5BhTYuTvP"
      },
      "source": [
        "# Modern Survival Analysis\n",
        "\n",
        "+ **Birth:** Customer joins Netflix  \n",
        "**Death:** Customer leaves Netflix  \n",
        "**Censorship:** At the current time, I cannot see all cancelations  \n",
        "  \n",
        "  \n",
        "+ **Birth:** Leader forms government  \n",
        "**Death:** Government dissolves  \n",
        "**Censorship:** Death of leader or current time do not allow me to see all dissolvements  \n",
        "\n",
        "\n",
        "+ **Birth:** Couple starts dating  \n",
        "**Death:** Couple breaks-up  \n",
        "**Censorship:** Some couples never break-up (partner's death comes first)  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVUDoqXOuTvU"
      },
      "source": [
        "First, let's take a dataset from lifelines to see what does it mean in practice."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IcqsnDunuZ3x"
      },
      "source": [
        "!pip install -q lifelines"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:10.331529Z",
          "start_time": "2020-01-09T22:37:03.811697Z"
        },
        "id": "A5qmUxSxuTvW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12eabc42-58f8-4ada-c523-27b3dc6c32dc"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "plt.style.use('seaborn')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-36032364a6f0>:4: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
            "  plt.style.use('seaborn')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Hz_oM2IuTve"
      },
      "source": [
        "from lifelines.datasets import load_dd\n",
        "\n",
        "df = load_dd()\n",
        "df = df[['ctryname', 'un_region_name', 'un_continent_name', 'ehead',\\\n",
        "         'democracy', 'regime', 'start_year', 'duration', 'observed']]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xCN-nZXDuTvk"
      },
      "source": [
        "# Democracy and dictatorship\n",
        "\n",
        "This dataset contains a classification of political regimes as democracy and dictatorship.\n",
        "* Classification of democracies as \n",
        "  + parliamentary, \n",
        "  + semi-presidential (mixed), and \n",
        "  + presidential. \n",
        "  \n",
        "* Classification of dictatorships as \n",
        "  + military, \n",
        "  + civilian, and \n",
        "  + royal. \n",
        "  \n",
        "Coverage: 202 countries, from 1946 or year of independence to 2008.\n",
        "\n",
        "**References**\n",
        "\n",
        "José Antonio Cheibub, Jennifer Gandhi, and James Raymond Vreeland. [\"Democracy and Dictatorship Revisited.\"](https://doi.org/10.1007/s11127-009-9491-2) Public Choice, vol. 143, no. 2-1, pp. 67-101, 2010."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:10.515360Z",
          "start_time": "2020-01-09T22:37:10.466697Z"
        },
        "id": "9b51kQDjuTvm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "bf51c087-3791-4120-c5db-2d5f25b491d1"
      },
      "source": [
        "df.tail(10).style.hide(axis=\"index\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f37eb2a3cd0>"
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              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_ba711\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_ba711_level0_col0\" class=\"col_heading level0 col0\" >ctryname</th>\n",
              "      <th id=\"T_ba711_level0_col1\" class=\"col_heading level0 col1\" >un_region_name</th>\n",
              "      <th id=\"T_ba711_level0_col2\" class=\"col_heading level0 col2\" >un_continent_name</th>\n",
              "      <th id=\"T_ba711_level0_col3\" class=\"col_heading level0 col3\" >ehead</th>\n",
              "      <th id=\"T_ba711_level0_col4\" class=\"col_heading level0 col4\" >democracy</th>\n",
              "      <th id=\"T_ba711_level0_col5\" class=\"col_heading level0 col5\" >regime</th>\n",
              "      <th id=\"T_ba711_level0_col6\" class=\"col_heading level0 col6\" >start_year</th>\n",
              "      <th id=\"T_ba711_level0_col7\" class=\"col_heading level0 col7\" >duration</th>\n",
              "      <th id=\"T_ba711_level0_col8\" class=\"col_heading level0 col8\" >observed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_ba711_row0_col0\" class=\"data row0 col0\" >Yugoslavia</td>\n",
              "      <td id=\"T_ba711_row0_col1\" class=\"data row0 col1\" >Southern Europe</td>\n",
              "      <td id=\"T_ba711_row0_col2\" class=\"data row0 col2\" >Europe</td>\n",
              "      <td id=\"T_ba711_row0_col3\" class=\"data row0 col3\" >Stipe Suvar</td>\n",
              "      <td id=\"T_ba711_row0_col4\" class=\"data row0 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_ba711_row0_col5\" class=\"data row0 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_ba711_row0_col6\" class=\"data row0 col6\" >1988</td>\n",
              "      <td id=\"T_ba711_row0_col7\" class=\"data row0 col7\" >1</td>\n",
              "      <td id=\"T_ba711_row0_col8\" class=\"data row0 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_ba711_row1_col0\" class=\"data row1 col0\" >Yugoslavia</td>\n",
              "      <td id=\"T_ba711_row1_col1\" class=\"data row1 col1\" >Southern Europe</td>\n",
              "      <td id=\"T_ba711_row1_col2\" class=\"data row1 col2\" >Europe</td>\n",
              "      <td id=\"T_ba711_row1_col3\" class=\"data row1 col3\" >Milan Pancevski</td>\n",
              "      <td id=\"T_ba711_row1_col4\" class=\"data row1 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_ba711_row1_col5\" class=\"data row1 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_ba711_row1_col6\" class=\"data row1 col6\" >1989</td>\n",
              "      <td id=\"T_ba711_row1_col7\" class=\"data row1 col7\" >1</td>\n",
              "      <td id=\"T_ba711_row1_col8\" class=\"data row1 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_ba711_row2_col0\" class=\"data row2 col0\" >Yugoslavia</td>\n",
              "      <td id=\"T_ba711_row2_col1\" class=\"data row2 col1\" >Southern Europe</td>\n",
              "      <td id=\"T_ba711_row2_col2\" class=\"data row2 col2\" >Europe</td>\n",
              "      <td id=\"T_ba711_row2_col3\" class=\"data row2 col3\" >Borisav Jovic</td>\n",
              "      <td id=\"T_ba711_row2_col4\" class=\"data row2 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_ba711_row2_col5\" class=\"data row2 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_ba711_row2_col6\" class=\"data row2 col6\" >1990</td>\n",
              "      <td id=\"T_ba711_row2_col7\" class=\"data row2 col7\" >1</td>\n",
              "      <td id=\"T_ba711_row2_col8\" class=\"data row2 col8\" >0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_ba711_row3_col0\" class=\"data row3 col0\" >Zambia</td>\n",
              "      <td id=\"T_ba711_row3_col1\" class=\"data row3 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_ba711_row3_col2\" class=\"data row3 col2\" >Africa</td>\n",
              "      <td id=\"T_ba711_row3_col3\" class=\"data row3 col3\" >Kenneth Kaunda</td>\n",
              "      <td id=\"T_ba711_row3_col4\" class=\"data row3 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_ba711_row3_col5\" class=\"data row3 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_ba711_row3_col6\" class=\"data row3 col6\" >1964</td>\n",
              "      <td id=\"T_ba711_row3_col7\" class=\"data row3 col7\" >27</td>\n",
              "      <td id=\"T_ba711_row3_col8\" class=\"data row3 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_ba711_row4_col0\" class=\"data row4 col0\" >Zambia</td>\n",
              "      <td id=\"T_ba711_row4_col1\" class=\"data row4 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_ba711_row4_col2\" class=\"data row4 col2\" >Africa</td>\n",
              "      <td id=\"T_ba711_row4_col3\" class=\"data row4 col3\" >Frederick Chiluba</td>\n",
              "      <td id=\"T_ba711_row4_col4\" class=\"data row4 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_ba711_row4_col5\" class=\"data row4 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_ba711_row4_col6\" class=\"data row4 col6\" >1991</td>\n",
              "      <td id=\"T_ba711_row4_col7\" class=\"data row4 col7\" >11</td>\n",
              "      <td id=\"T_ba711_row4_col8\" class=\"data row4 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_ba711_row5_col0\" class=\"data row5 col0\" >Zambia</td>\n",
              "      <td id=\"T_ba711_row5_col1\" class=\"data row5 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_ba711_row5_col2\" class=\"data row5 col2\" >Africa</td>\n",
              "      <td id=\"T_ba711_row5_col3\" class=\"data row5 col3\" >Levy Patrick Mwanawasa</td>\n",
              "      <td id=\"T_ba711_row5_col4\" class=\"data row5 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_ba711_row5_col5\" class=\"data row5 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_ba711_row5_col6\" class=\"data row5 col6\" >2002</td>\n",
              "      <td id=\"T_ba711_row5_col7\" class=\"data row5 col7\" >6</td>\n",
              "      <td id=\"T_ba711_row5_col8\" class=\"data row5 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_ba711_row6_col0\" class=\"data row6 col0\" >Zambia</td>\n",
              "      <td id=\"T_ba711_row6_col1\" class=\"data row6 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_ba711_row6_col2\" class=\"data row6 col2\" >Africa</td>\n",
              "      <td id=\"T_ba711_row6_col3\" class=\"data row6 col3\" >Rupiah Bwezani Banda</td>\n",
              "      <td id=\"T_ba711_row6_col4\" class=\"data row6 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_ba711_row6_col5\" class=\"data row6 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_ba711_row6_col6\" class=\"data row6 col6\" >2008</td>\n",
              "      <td id=\"T_ba711_row6_col7\" class=\"data row6 col7\" >1</td>\n",
              "      <td id=\"T_ba711_row6_col8\" class=\"data row6 col8\" >0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_ba711_row7_col0\" class=\"data row7 col0\" >Zimbabwe</td>\n",
              "      <td id=\"T_ba711_row7_col1\" class=\"data row7 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_ba711_row7_col2\" class=\"data row7 col2\" >Africa</td>\n",
              "      <td id=\"T_ba711_row7_col3\" class=\"data row7 col3\" >Ian Smith</td>\n",
              "      <td id=\"T_ba711_row7_col4\" class=\"data row7 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_ba711_row7_col5\" class=\"data row7 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_ba711_row7_col6\" class=\"data row7 col6\" >1965</td>\n",
              "      <td id=\"T_ba711_row7_col7\" class=\"data row7 col7\" >14</td>\n",
              "      <td id=\"T_ba711_row7_col8\" class=\"data row7 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_ba711_row8_col0\" class=\"data row8 col0\" >Zimbabwe</td>\n",
              "      <td id=\"T_ba711_row8_col1\" class=\"data row8 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_ba711_row8_col2\" class=\"data row8 col2\" >Africa</td>\n",
              "      <td id=\"T_ba711_row8_col3\" class=\"data row8 col3\" >Abel Muzorewa</td>\n",
              "      <td id=\"T_ba711_row8_col4\" class=\"data row8 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_ba711_row8_col5\" class=\"data row8 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_ba711_row8_col6\" class=\"data row8 col6\" >1979</td>\n",
              "      <td id=\"T_ba711_row8_col7\" class=\"data row8 col7\" >1</td>\n",
              "      <td id=\"T_ba711_row8_col8\" class=\"data row8 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_ba711_row9_col0\" class=\"data row9 col0\" >Zimbabwe</td>\n",
              "      <td id=\"T_ba711_row9_col1\" class=\"data row9 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_ba711_row9_col2\" class=\"data row9 col2\" >Africa</td>\n",
              "      <td id=\"T_ba711_row9_col3\" class=\"data row9 col3\" >Robert Mugabe</td>\n",
              "      <td id=\"T_ba711_row9_col4\" class=\"data row9 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_ba711_row9_col5\" class=\"data row9 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_ba711_row9_col6\" class=\"data row9 col6\" >1980</td>\n",
              "      <td id=\"T_ba711_row9_col7\" class=\"data row9 col7\" >29</td>\n",
              "      <td id=\"T_ba711_row9_col8\" class=\"data row9 col8\" >0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-o57OZxvuTvu"
      },
      "source": [
        "Let's look at right-censored samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:12.185066Z",
          "start_time": "2020-01-09T22:37:10.519270Z"
        },
        "scrolled": false,
        "id": "vzaXN9QWuTvv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "48bcd2d2-f156-4e0b-f205-2f7dfea6f625"
      },
      "source": [
        "format_dict = {'ehead':'{}','duration':'{}', 'observed':'{}'}\n",
        "(df.query('ctryname == \"United States of America\"')[['ehead', 'duration', 'observed']].style.format(format_dict)\n",
        "  .hide(axis=\"index\")\n",
        "  .highlight_min('observed', color='lightgreen'))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<style type=\"text/css\">\n",
              "#T_7cf4c_row2_col2, #T_7cf4c_row10_col2 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_7cf4c\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_7cf4c_level0_col0\" class=\"col_heading level0 col0\" >ehead</th>\n",
              "      <th id=\"T_7cf4c_level0_col1\" class=\"col_heading level0 col1\" >duration</th>\n",
              "      <th id=\"T_7cf4c_level0_col2\" class=\"col_heading level0 col2\" >observed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_7cf4c_row0_col0\" class=\"data row0 col0\" >Harry Truman</td>\n",
              "      <td id=\"T_7cf4c_row0_col1\" class=\"data row0 col1\" >7</td>\n",
              "      <td id=\"T_7cf4c_row0_col2\" class=\"data row0 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_7cf4c_row1_col0\" class=\"data row1 col0\" >Dwight D. Eisenhower</td>\n",
              "      <td id=\"T_7cf4c_row1_col1\" class=\"data row1 col1\" >8</td>\n",
              "      <td id=\"T_7cf4c_row1_col2\" class=\"data row1 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_7cf4c_row2_col0\" class=\"data row2 col0\" >John Kennedy</td>\n",
              "      <td id=\"T_7cf4c_row2_col1\" class=\"data row2 col1\" >2</td>\n",
              "      <td id=\"T_7cf4c_row2_col2\" class=\"data row2 col2\" >0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_7cf4c_row3_col0\" class=\"data row3 col0\" >Lyndon Johnson</td>\n",
              "      <td id=\"T_7cf4c_row3_col1\" class=\"data row3 col1\" >6</td>\n",
              "      <td id=\"T_7cf4c_row3_col2\" class=\"data row3 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_7cf4c_row4_col0\" class=\"data row4 col0\" >Richard Nixon</td>\n",
              "      <td id=\"T_7cf4c_row4_col1\" class=\"data row4 col1\" >5</td>\n",
              "      <td id=\"T_7cf4c_row4_col2\" class=\"data row4 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_7cf4c_row5_col0\" class=\"data row5 col0\" >Gerald Ford</td>\n",
              "      <td id=\"T_7cf4c_row5_col1\" class=\"data row5 col1\" >3</td>\n",
              "      <td id=\"T_7cf4c_row5_col2\" class=\"data row5 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_7cf4c_row6_col0\" class=\"data row6 col0\" >Jimmy Carter</td>\n",
              "      <td id=\"T_7cf4c_row6_col1\" class=\"data row6 col1\" >4</td>\n",
              "      <td id=\"T_7cf4c_row6_col2\" class=\"data row6 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_7cf4c_row7_col0\" class=\"data row7 col0\" >Ronald Reagan</td>\n",
              "      <td id=\"T_7cf4c_row7_col1\" class=\"data row7 col1\" >8</td>\n",
              "      <td id=\"T_7cf4c_row7_col2\" class=\"data row7 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_7cf4c_row8_col0\" class=\"data row8 col0\" >George Bush</td>\n",
              "      <td id=\"T_7cf4c_row8_col1\" class=\"data row8 col1\" >4</td>\n",
              "      <td id=\"T_7cf4c_row8_col2\" class=\"data row8 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_7cf4c_row9_col0\" class=\"data row9 col0\" >Bill Clinton</td>\n",
              "      <td id=\"T_7cf4c_row9_col1\" class=\"data row9 col1\" >8</td>\n",
              "      <td id=\"T_7cf4c_row9_col2\" class=\"data row9 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_7cf4c_row10_col0\" class=\"data row10 col0\" >George W. Bush</td>\n",
              "      <td id=\"T_7cf4c_row10_col1\" class=\"data row10 col1\" >8</td>\n",
              "      <td id=\"T_7cf4c_row10_col2\" class=\"data row10 col2\" >0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:12.216114Z",
          "start_time": "2020-01-09T22:37:12.189453Z"
        },
        "id": "AVHDxLziuTv2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fe19217-901e-44c9-cfd8-7e4bf302521e"
      },
      "source": [
        "print(f'samples: {len(df)}\\n')\n",
        "print(f'right censored samples: {len(df.query(\"observed == 0\"))}')\n",
        "print(f'right censored samples (%): {100*len(df.query(\"observed == 0\"))/len(df):.1f}%')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "samples: 1808\n",
            "\n",
            "right censored samples: 340\n",
            "right censored samples (%): 18.8%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3v0OGKU_uTv9"
      },
      "source": [
        "# How can we estimate the probability of a government survival?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J0UzmdpeuTv9"
      },
      "source": [
        "**Example:** I want to estimate the survival function of a new machine and I have 100 of these new machines. After the first year:\n",
        "\n",
        "Samples | I \n",
        "--- | --- \n",
        "Initial numbers | 100 \n",
        "Deaths in first year of age | 70 \n",
        "One-year survivors | `30` \n",
        "\n",
        "Therefore, a reasonable estimate of the survival probability of 1 year is 0.3.\n",
        "\n",
        "I have increased my production. So now I have 1000 new machines.\n",
        "\n",
        "Samples | I | II\n",
        "--- | --- | ---\n",
        "Initial numbers | 100 | 1000\n",
        "Deaths in first year of age | 70 | 750\n",
        "One-year survivors | `30` | `250`\n",
        "Deaths in second year of age | 15 | \n",
        "Two-year survivors | `15` | \n",
        "\n",
        "What would be a good estimate of the survival probability of 1 year? and 2 years?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVevvzzCuTv-"
      },
      "source": [
        "The estimate of the probability of survival of 1 year would be \n",
        "$\\hat{P}(1)=(30+250)/(100+1000) \\sim 0.255$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BlBS5dveuTwA"
      },
      "source": [
        "$\\hat{P}(2|1) = 15/30 = 0.5$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVja8-P5uTwA"
      },
      "source": [
        "$\\hat{P}(2)=0.255\\times0.5=0.127$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pG5yhITUuTwB"
      },
      "source": [
        "# Kaplan-Meier estimator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E6hn2l6PuTwC"
      },
      "source": [
        "**Definition:** Kaplan-Meier estimator of the survival function is given by\n",
        "\n",
        "$$\n",
        "\\hat{S}(t):=\\prod_{i:t_i\\le t}\\left(1-\\frac{d_i}{n_i}\\right)\n",
        "$$\n",
        "where $t_i$ is a time where at least one event happened, $d_i$ the number of events that happened at time $t_i$, and $n_i$ the individuals known to have survived up to time $t_i$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cojEUEjsuTwD"
      },
      "source": [
        "We use the [Kaplan-Meier estimator](https://en.wikipedia.org/wiki/Kaplan?Meier_estimator) to estimate the probability of a government survival."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:16.155051Z",
          "start_time": "2020-01-09T22:37:12.219801Z"
        },
        "scrolled": false,
        "id": "LCGcjoexuTwE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "outputId": "fc7d6d45-90d8-4840-ccb7-c7bf4a78482d"
      },
      "source": [
        "from lifelines import KaplanMeierFitter\n",
        "kmf = KaplanMeierFitter()\n",
        "kmf.fit(df['duration'],df['observed'], label='Estimate for average government')\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "kmf.plot(ax=ax)\n",
        "plt.title('Estimated probability of government survival vs number of years')\n",
        "plt.xlabel('Time (in years)')\n",
        "plt.ylabel('Estimated probability of government survival')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdrG0N6nuTwL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc2f59b8-2808-458c-c19e-ea1c9db271c7"
      },
      "source": [
        "print(f'The median number of years of government survival is {kmf.median_survival_time_}')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The median number of years of government survival is 4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "NQHPG2x6uTwQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "outputId": "5f1f3007-927c-4b24-b3a6-0f08aa22b93c"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "for r in df['democracy'].unique():\n",
        "    ix = df['democracy'] == r\n",
        "    kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
        "    kmf.plot(ax=ax)\n",
        "plt.title('Estimated probability of government survival vs number of years')\n",
        "plt.xlabel('Time (in years)')\n",
        "plt.ylabel('Estimated probability of government survival')\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBKPtOFKuTwV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b6b1579-a5a4-4305-de4a-4c50007ff5eb"
      },
      "source": [
        "for r in df['democracy'].unique():\n",
        "    ix = df['democracy'] == r\n",
        "    kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
        "    print(f'The median number of years for a {r} is {kmf.median_survival_time_}')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The median number of years for a Non-democracy is 6.0\n",
            "The median number of years for a Democracy is 3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhK-X5-fuTwc"
      },
      "source": [
        "How can we tell if these survival functions are different?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uL59D8GAuTwc"
      },
      "source": [
        "# Log-rank test (not recommended but still very common statistical test)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "LDq8D5ovuTwe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbb3f3a5-d512-4608-aaa4-b5856bf7601a"
      },
      "source": [
        "df['democracy'].unique()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Non-democracy', 'Democracy'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kaRMHNtSuTwi"
      },
      "source": [
        "from lifelines.statistics import logrank_test"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qrrTDDWuTwn"
      },
      "source": [
        "ix = df['democracy'] == 'Democracy'\n",
        "T_democracy, E_democracy = df.loc[ix, 'duration'], df.loc[ix, 'observed']\n",
        "T_non_democracy, E_non_democracy = df.loc[~ix, 'duration'], df.loc[~ix, 'observed']"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Qw20wiiRuTws",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2a54d991-9e84-469e-f1f6-9fe872d356bf"
      },
      "source": [
        "results = logrank_test(T_democracy, T_non_democracy, event_observed_A=E_democracy, event_observed_B=E_non_democracy)\n",
        "results.print_summary()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<lifelines.StatisticalResult: logrank_test>\n",
              "               t_0 = -1\n",
              " null_distribution = chi squared\n",
              "degrees_of_freedom = 1\n",
              "         test_name = logrank_test\n",
              "\n",
              "---\n",
              " test_statistic      p  -log2(p)\n",
              "         260.47 <0.005    192.23"
            ],
            "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>t_0</th>\n",
              "      <td>-1</td>\n",
              "    </tr>\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>test_name</th>\n",
              "      <td>logrank_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>test_statistic</th>\n",
              "      <th>p</th>\n",
              "      <th>-log2(p)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>260.47</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>192.23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/latex": "\\begin{tabular}{lrrr}\n & test_statistic & p & -log2(p) \\\\\n0 & 260.47 & 0.00 & 192.23 \\\\\n\\end{tabular}\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Oms68oOuTww",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "125c3e08-ab6f-4007-ba65-03451f0bd54b"
      },
      "source": [
        "print(results.p_value)\n",
        "print(results.test_statistic)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.3557143218482446e-58\n",
            "260.46953907795944\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTX47C6-uTw1"
      },
      "source": [
        "# Univariate Cox regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "meyGKaYcuTw4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "outputId": "059710cd-7856-42ad-d9fc-263639d60a69"
      },
      "source": [
        "from lifelines import CoxPHFitter\n",
        "\n",
        "cph = CoxPHFitter()\n",
        "df_Uni_Cox = df.copy()\n",
        "df_Uni_Cox['indicator'] = df_Uni_Cox['democracy'] == 'Democracy'\n",
        "cph.fit(df_Uni_Cox[['indicator', 'duration', 'observed']], 'duration', 'observed')\n",
        "cph.print_summary()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<lifelines.CoxPHFitter: fitted with 1808 total observations, 340 right-censored observations>\n",
              "             duration col = 'duration'\n",
              "                event col = 'observed'\n",
              "      baseline estimation = breslow\n",
              "   number of observations = 1808\n",
              "number of events observed = 1468\n",
              "   partial log-likelihood = -9614.27\n",
              "         time fit was run = 2023-06-07 14:03:22 UTC\n",
              "\n",
              "---\n",
              "            coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
              "covariate                                                                                                         \n",
              "indicator   0.96       2.62       0.06             0.84             1.09                 2.32                 2.96\n",
              "\n",
              "            cmp to     z      p   -log2(p)\n",
              "covariate                                 \n",
              "indicator     0.00 15.40 <0.005     175.43\n",
              "---\n",
              "Concordance = 0.59\n",
              "Partial AIC = 19230.53\n",
              "log-likelihood ratio test = 264.03 on 1 df\n",
              "-log2(p) of ll-ratio test = 194.81"
            ],
            "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>model</th>\n",
              "      <td>lifelines.CoxPHFitter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration col</th>\n",
              "      <td>'duration'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>event col</th>\n",
              "      <td>'observed'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>baseline estimation</th>\n",
              "      <td>breslow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of observations</th>\n",
              "      <td>1808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of events observed</th>\n",
              "      <td>1468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial log-likelihood</th>\n",
              "      <td>-9614.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time fit was run</th>\n",
              "      <td>2023-06-07 14:03:22 UTC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th style=\"min-width: 12px;\"></th>\n",
              "      <th style=\"min-width: 12px;\">coef</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">cmp to</th>\n",
              "      <th style=\"min-width: 12px;\">z</th>\n",
              "      <th style=\"min-width: 12px;\">p</th>\n",
              "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>indicator</th>\n",
              "      <td>0.96</td>\n",
              "      <td>2.62</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.84</td>\n",
              "      <td>1.09</td>\n",
              "      <td>2.32</td>\n",
              "      <td>2.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>15.40</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>175.43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><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>Concordance</th>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Partial AIC</th>\n",
              "      <td>19230.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log-likelihood ratio test</th>\n",
              "      <td>264.03 on 1 df</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-log2(p) of ll-ratio test</th>\n",
              "      <td>194.81</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/latex": "\\begin{tabular}{lrrrrrrrrrrr}\n & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\ncovariate &  &  &  &  &  &  &  &  &  &  &  \\\\\nindicator & 0.96 & 2.62 & 0.06 & 0.84 & 1.09 & 2.32 & 2.96 & 0.00 & 15.40 & 0.00 & 175.43 \\\\\n\\end{tabular}\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:17.131185Z",
          "start_time": "2020-01-09T22:37:16.573714Z"
        },
        "id": "USPOd2fOuTw8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "outputId": "b6c28d28-4b85-41d6-e41e-bc8d939b9a6f"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "\n",
        "for r in df['regime'].unique():\n",
        "    ix = df['regime'] == r\n",
        "    kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
        "    kmf.survival_function_.plot(ax=ax)\n",
        "plt.title('Estimated probability of government survival vs number of years')\n",
        "plt.xlabel('Time (in years)')\n",
        "plt.ylabel('Estimated probability of government survival')\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:17.690930Z",
          "start_time": "2020-01-09T22:37:17.134877Z"
        },
        "scrolled": false,
        "id": "ydmaYBqnuTxA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "outputId": "091d612e-666b-4a94-b84f-1fd2bc1c5d2e"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "for r in df['un_continent_name'].unique():\n",
        "    ix = df['un_continent_name'] == r\n",
        "    kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
        "    kmf.plot(ax=ax)\n",
        "plt.title('Estimated probability of government survival vs number of years')\n",
        "plt.xlabel('Time (in years)')\n",
        "plt.ylabel('Estimated probability of government survival')\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:17.723699Z",
          "start_time": "2020-01-09T22:37:17.694880Z"
        },
        "scrolled": false,
        "id": "NXIl1sBfuTxF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "2da35a85-44e8-4937-ea32-e2e371cdc789"
      },
      "source": [
        "df.query('ctryname == \"United States of America\"').tail(3)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      ctryname    un_region_name un_continent_name  \\\n",
              "1721  United States of America  Northern America          Americas   \n",
              "1722  United States of America  Northern America          Americas   \n",
              "1723  United States of America  Northern America          Americas   \n",
              "\n",
              "               ehead  democracy            regime  start_year  duration  \\\n",
              "1721     George Bush  Democracy  Presidential Dem        1989         4   \n",
              "1722    Bill Clinton  Democracy  Presidential Dem        1993         8   \n",
              "1723  George W. Bush  Democracy  Presidential Dem        2001         8   \n",
              "\n",
              "      observed  \n",
              "1721         1  \n",
              "1722         1  \n",
              "1723         0  "
            ],
            "text/html": [
              "\n",
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              "      <td>United States of America</td>\n",
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              "      <td>Americas</td>\n",
              "      <td>George Bush</td>\n",
              "      <td>Democracy</td>\n",
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              "      <td>George W. Bush</td>\n",
              "      <td>Democracy</td>\n",
              "      <td>Presidential Dem</td>\n",
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              "\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-adfadf48-19ba-42de-8313-9f5f5750f16a 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-adfadf48-19ba-42de-8313-9f5f5750f16a');\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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:17.748036Z",
          "start_time": "2020-01-09T22:37:17.725349Z"
        },
        "scrolled": false,
        "id": "Qzy7VJ_luTxI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "d13be296-0dd3-4ebb-ff63-fea522bf6631"
      },
      "source": [
        "df.query('ctryname == \"United Kingdom\"').tail(3)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            ctryname   un_region_name un_continent_name         ehead  \\\n",
              "1710  United Kingdom  Northern Europe            Europe    John Major   \n",
              "1711  United Kingdom  Northern Europe            Europe    Tony Blair   \n",
              "1712  United Kingdom  Northern Europe            Europe  Gordon Brown   \n",
              "\n",
              "      democracy             regime  start_year  duration  observed  \n",
              "1710  Democracy  Parliamentary Dem        1990         7         1  \n",
              "1711  Democracy  Parliamentary Dem        1997        10         1  \n",
              "1712  Democracy  Parliamentary Dem        2007         2         0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5fef7fa5-e38d-42d5-ba2a-70b3400e0976\">\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>ctryname</th>\n",
              "      <th>un_region_name</th>\n",
              "      <th>un_continent_name</th>\n",
              "      <th>ehead</th>\n",
              "      <th>democracy</th>\n",
              "      <th>regime</th>\n",
              "      <th>start_year</th>\n",
              "      <th>duration</th>\n",
              "      <th>observed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1710</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>Northern Europe</td>\n",
              "      <td>Europe</td>\n",
              "      <td>John Major</td>\n",
              "      <td>Democracy</td>\n",
              "      <td>Parliamentary Dem</td>\n",
              "      <td>1990</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1711</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>Northern Europe</td>\n",
              "      <td>Europe</td>\n",
              "      <td>Tony Blair</td>\n",
              "      <td>Democracy</td>\n",
              "      <td>Parliamentary Dem</td>\n",
              "      <td>1997</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1712</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>Northern Europe</td>\n",
              "      <td>Europe</td>\n",
              "      <td>Gordon Brown</td>\n",
              "      <td>Democracy</td>\n",
              "      <td>Parliamentary Dem</td>\n",
              "      <td>2007</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5fef7fa5-e38d-42d5-ba2a-70b3400e0976')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://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-5fef7fa5-e38d-42d5-ba2a-70b3400e0976 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-5fef7fa5-e38d-42d5-ba2a-70b3400e0976');\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": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:18.129226Z",
          "start_time": "2020-01-09T22:37:17.749639Z"
        },
        "id": "010659VeuTxM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "outputId": "8807eb92-0489-4000-e950-a916beb71732"
      },
      "source": [
        "ix_US = df['ctryname'] == 'United States of America'\n",
        "ix_UK = df['ctryname'] == 'United Kingdom'\n",
        "\n",
        "kmf_US = KaplanMeierFitter()\n",
        "kmf_US.fit(df['duration'].loc[ix_US], df['observed'].loc[ix_US], label='USA')\n",
        "\n",
        "kmf_UK = KaplanMeierFitter()\n",
        "kmf_UK.fit(df['duration'].loc[ix_UK], df['observed'].loc[ix_UK], label='UK')\n",
        "\n",
        "plt.figure(figsize=(10,7))\n",
        "ax = plt.subplot(111)\n",
        "kmf_US.plot(ax=ax)\n",
        "kmf_UK.plot(ax=ax)\n",
        "plt.title('Estimated probability of government survival vs number of years')\n",
        "plt.xlabel('Time (in years)')\n",
        "plt.ylabel('Estimated probability of government survival')\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "nCPi7YY1uTxQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1febb61f-a96b-44a1-8486-ee0d4c7635d4"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ctryname', 'un_region_name', 'un_continent_name', 'ehead', 'democracy',\n",
              "       'regime', 'start_year', 'duration', 'observed'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "xphf-zpauTxU"
      },
      "source": [
        "df = df.drop(columns=['ctryname', 'un_region_name', 'ehead', 'regime', 'start_year'])"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NOuRvPJXuTxZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52238a05-ab94-49cf-8616-db7290b37999"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['un_continent_name', 'democracy', 'duration', 'observed'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzcg35DtuTxd"
      },
      "source": [
        "# df_hazard = pd.get_dummies(df, drop_first=True, columns=df.columns.drop(['duration', 'observed']))\n",
        "df_hazard = pd.get_dummies(df, columns=df.columns.drop(['duration', 'observed']))"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGxRvM20uTxg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b27d853-3303-48d4-adb3-310aac3c5237"
      },
      "source": [
        "df_hazard.columns"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['duration', 'observed', 'un_continent_name_Africa',\n",
              "       'un_continent_name_Americas', 'un_continent_name_Asia',\n",
              "       'un_continent_name_Europe', 'un_continent_name_Oceania',\n",
              "       'democracy_Democracy', 'democracy_Non-democracy'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9b7PSd2uTxl"
      },
      "source": [
        "df_hazard = df_hazard.drop(columns=['un_continent_name_Americas', 'democracy_Democracy'])"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-O2MTLPuTxo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "010a93c9-2db1-4d29-f2f0-3a4b7cfd18f5"
      },
      "source": [
        "df_hazard.columns"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['duration', 'observed', 'un_continent_name_Africa',\n",
              "       'un_continent_name_Asia', 'un_continent_name_Europe',\n",
              "       'un_continent_name_Oceania', 'democracy_Non-democracy'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VaQCN1ChuTxs"
      },
      "source": [
        "# Multivariate Cox regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUKzqcFvuTxs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "outputId": "ed4bf558-693e-442e-cda2-73ef93e6efd7"
      },
      "source": [
        "from lifelines import CoxPHFitter\n",
        "\n",
        "cph = CoxPHFitter(penalizer=0.1)\n",
        "cph.fit(df_hazard, 'duration', 'observed')\n",
        "cph.print_summary()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<lifelines.CoxPHFitter: fitted with 1808 total observations, 340 right-censored observations>\n",
              "             duration col = 'duration'\n",
              "                event col = 'observed'\n",
              "                penalizer = 0.1\n",
              "                 l1 ratio = 0.0\n",
              "      baseline estimation = breslow\n",
              "   number of observations = 1808\n",
              "number of events observed = 1468\n",
              "   partial log-likelihood = -9613.12\n",
              "         time fit was run = 2023-06-07 14:03:24 UTC\n",
              "\n",
              "---\n",
              "                            coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
              "covariate                                                                                                                         \n",
              "un_continent_name_Africa   -0.20       0.82       0.08            -0.36            -0.04                 0.70                 0.96\n",
              "un_continent_name_Asia     -0.06       0.95       0.07            -0.20             0.09                 0.82                 1.09\n",
              "un_continent_name_Europe    0.23       1.26       0.06             0.11             0.35                 1.12                 1.43\n",
              "un_continent_name_Oceania  -0.12       0.89       0.11            -0.33             0.10                 0.72                 1.10\n",
              "democracy_Non-democracy    -0.72       0.49       0.06            -0.84            -0.60                 0.43                 0.55\n",
              "\n",
              "                            cmp to      z      p   -log2(p)\n",
              "covariate                                                  \n",
              "un_continent_name_Africa      0.00  -2.48   0.01       6.25\n",
              "un_continent_name_Asia        0.00  -0.77   0.44       1.19\n",
              "un_continent_name_Europe      0.00   3.79 <0.005      12.70\n",
              "un_continent_name_Oceania     0.00  -1.07   0.29       1.80\n",
              "democracy_Non-democracy       0.00 -11.48 <0.005      98.97\n",
              "---\n",
              "Concordance = 0.62\n",
              "Partial AIC = 19236.25\n",
              "log-likelihood ratio test = 266.31 on 5 df\n",
              "-log2(p) of ll-ratio test = 181.91"
            ],
            "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>model</th>\n",
              "      <td>lifelines.CoxPHFitter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration col</th>\n",
              "      <td>'duration'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>event col</th>\n",
              "      <td>'observed'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>penalizer</th>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l1 ratio</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>baseline estimation</th>\n",
              "      <td>breslow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of observations</th>\n",
              "      <td>1808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of events observed</th>\n",
              "      <td>1468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial log-likelihood</th>\n",
              "      <td>-9613.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time fit was run</th>\n",
              "      <td>2023-06-07 14:03:24 UTC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th style=\"min-width: 12px;\"></th>\n",
              "      <th style=\"min-width: 12px;\">coef</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">cmp to</th>\n",
              "      <th style=\"min-width: 12px;\">z</th>\n",
              "      <th style=\"min-width: 12px;\">p</th>\n",
              "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>un_continent_name_Africa</th>\n",
              "      <td>-0.20</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-2.48</td>\n",
              "      <td>0.01</td>\n",
              "      <td>6.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>un_continent_name_Asia</th>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.07</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.82</td>\n",
              "      <td>1.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.77</td>\n",
              "      <td>0.44</td>\n",
              "      <td>1.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>un_continent_name_Europe</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.26</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.35</td>\n",
              "      <td>1.12</td>\n",
              "      <td>1.43</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.79</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>12.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>un_continent_name_Oceania</th>\n",
              "      <td>-0.12</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.11</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1.10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-1.07</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>democracy_Non-democracy</th>\n",
              "      <td>-0.72</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.06</td>\n",
              "      <td>-0.84</td>\n",
              "      <td>-0.60</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-11.48</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>98.97</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><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>Concordance</th>\n",
              "      <td>0.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Partial AIC</th>\n",
              "      <td>19236.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log-likelihood ratio test</th>\n",
              "      <td>266.31 on 5 df</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-log2(p) of ll-ratio test</th>\n",
              "      <td>181.91</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/latex": "\\begin{tabular}{lrrrrrrrrrrr}\n & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\ncovariate &  &  &  &  &  &  &  &  &  &  &  \\\\\nun_continent_name_Africa & -0.20 & 0.82 & 0.08 & -0.36 & -0.04 & 0.70 & 0.96 & 0.00 & -2.48 & 0.01 & 6.25 \\\\\nun_continent_name_Asia & -0.06 & 0.95 & 0.07 & -0.20 & 0.09 & 0.82 & 1.09 & 0.00 & -0.77 & 0.44 & 1.19 \\\\\nun_continent_name_Europe & 0.23 & 1.26 & 0.06 & 0.11 & 0.35 & 1.12 & 1.43 & 0.00 & 3.79 & 0.00 & 12.70 \\\\\nun_continent_name_Oceania & -0.12 & 0.89 & 0.11 & -0.33 & 0.10 & 0.72 & 1.10 & 0.00 & -1.07 & 0.29 & 1.80 \\\\\ndemocracy_Non-democracy & -0.72 & 0.49 & 0.06 & -0.84 & -0.60 & 0.43 & 0.55 & 0.00 & -11.48 & 0.00 & 98.97 \\\\\n\\end{tabular}\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYkJ1coruTxx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "outputId": "6e800a3e-0e2a-413c-8f64-5df9897e4e55"
      },
      "source": [
        "fig_coef, ax_coef = plt.subplots(figsize=(12,7))\n",
        "ax_coef.set_title('Survival Regression: Coefficients and Confident Intervals')\n",
        "cph.plot(ax=ax_coef)\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3hWrJk8uTx1"
      },
      "source": [
        "from google.colab import files\n",
        "files.download(\"Survival-Regression.png\")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}