{
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   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from lifelines import CoxPHFitter\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the proportional hazard assumptions\n",
    "\n",
    "This Jupyter notebook is a small tutorial on how to test and fix proportional hazard problems. \n",
    "\n",
    "The proportional hazard assumption is that _all_ individuals have the same hazard function, but a unique scaling factor infront. So the _shape_ of the hazard function is the same for all individuals, and only a scalar infront changes. \n",
    "\n",
    "$$h_i(t) = a_i h(t)$$\n",
    "\n",
    "At the core of the  assumption is that $a_i$ is not time varying, that is, $a_i(t) = a_i$. Further more, if we take the ratio of this with another subject (called the hazard ratio): \n",
    "\n",
    "$$\\frac{h_i(t)}{h_j(t)} = \\frac{a_i h(t)}{a_j h(t)} = \\frac{a_i}{a_j}$$\n",
    "\n",
    "is constant for all $t$. In this tutorial we will test this non-time varying assumption, and look at ways to handle violations. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxPHFitter:\"None\", fitted with 432 total observations, 318 right-censored observations>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifelines.datasets import load_rossi\n",
    "rossi = load_rossi()\n",
    "cph = CoxPHFitter()\n",
    "\n",
    "cph.fit(rossi, 'week', 'arrest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  \n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'week'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-658.748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2019-11-17 14:00:03 UTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>untransformed variables</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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      "text/html": [
       "<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>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>-0.379</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.191</td>\n",
       "      <td>-0.755</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.996</td>\n",
       "      <td>-1.983</td>\n",
       "      <td>0.047</td>\n",
       "      <td>4.398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.057</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-0.101</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.986</td>\n",
       "      <td>-2.611</td>\n",
       "      <td>0.009</td>\n",
       "      <td>6.791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.314</td>\n",
       "      <td>1.369</td>\n",
       "      <td>0.308</td>\n",
       "      <td>-0.290</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.748</td>\n",
       "      <td>2.503</td>\n",
       "      <td>1.019</td>\n",
       "      <td>0.308</td>\n",
       "      <td>1.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wexp</th>\n",
       "      <td>-0.150</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.212</td>\n",
       "      <td>-0.566</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.568</td>\n",
       "      <td>1.305</td>\n",
       "      <td>-0.706</td>\n",
       "      <td>0.480</td>\n",
       "      <td>1.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>-0.434</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.382</td>\n",
       "      <td>-1.182</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.307</td>\n",
       "      <td>1.370</td>\n",
       "      <td>-1.136</td>\n",
       "      <td>0.256</td>\n",
       "      <td>1.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>-0.085</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.196</td>\n",
       "      <td>-0.469</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.626</td>\n",
       "      <td>1.348</td>\n",
       "      <td>-0.434</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>0.091</td>\n",
       "      <td>1.096</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.148</td>\n",
       "      <td>1.036</td>\n",
       "      <td>1.159</td>\n",
       "      <td>3.194</td>\n",
       "      <td>0.001</td>\n",
       "      <td>9.476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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   ],
   "source": [
    "cph.print_summary(model=\"untransformed variables\", decimals=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking assumptions with `check_assumptions`\n",
    "\n",
    "New to lifelines 0.16.0 is the `CoxPHFitter.check_assumptions` method. This method will compute statistics that check the proportional hazard assumption, produce plots to check assumptions, and more. Also included is an option to display advice to the console. Here's a breakdown of each information displayed:\n",
    "\n",
    " - Presented first are the results of a statistical test to test for any time-varying coefficients. A time-varying coefficient imply a covariate's influence _relative to the baseline_ changes over time. This implies a violation of the proportional hazard assumption. For each variable, we transform _time_ four times (these are common transformations of time to perform). If _lifelines_ rejects the null (that is, _lifelines_ rejects that the coefficient is not time-varying), we report this to the user.\n",
    " - Some advice is presented on how to correct the proportional hazard violation based on some summary statistics of the variable. \n",
    " - As a compliment to the above statistical test, for each variable that violates the PH assumption, visual plots of the the _scaled Schoenfeld residuals_ is presented against the four time transformations. A fitted lowess is also presented, along with 10 bootstrapped lowess lines (as an approximation to the confidence interval of the original lowess line). Ideally, this lowess line is constant (flat). Deviations away from the constant line are violations of the PH assumption. \n",
    " \n",
    "####  Why the _scaled Schoenfeld residuals_?\n",
    " \n",
    "This section can be skipped on first read. Let $s_{t,j}$ denote the scaled Schoenfeld residuals of variable $j$ at time $t$, $\\hat{\\beta_j}$ denote the maximum-likelihood estimate of the $j$th variable, and $\\beta_j(t)$ a time-varying coefficient in (fictional) alternative model that allows for time-varying coefficients. Therneau and Grambsch showed that. \n",
    "\n",
    "$$E[s_{t,j}] + \\hat{\\beta_j} = \\beta_j(t)$$\n",
    "\n",
    "The proportional hazard assumption implies that $\\hat{\\beta_j} = \\beta_j(t)$, hence $E[s_{t,j}] = 0$. This is what the above proportional hazard test is testing. Visually, plotting $s_{t,j}$ over time (or some transform of time), is a good way to see violations of $E[s_{t,j}] = 0$, along with the statisical test. "
   ]
  },
  {
   "cell_type": "code",
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     "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",
      "<lifelines.StatisticalResult>\n",
      "         test_name = proportional_hazard_test\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 1\n",
      "\n",
      "---\n",
      "           test_statistic      p  -log2(p)\n",
      "age  km             11.03 <0.005     10.12\n",
      "     rank           11.09 <0.005     10.17\n",
      "fin  km              0.02   0.89      0.17\n",
      "     rank            0.02   0.90      0.16\n",
      "mar  km              0.60   0.44      1.19\n",
      "     rank            0.67   0.41      1.27\n",
      "paro km              0.12   0.73      0.45\n",
      "     rank            0.14   0.71      0.49\n",
      "prio km              0.02   0.88      0.18\n",
      "     rank            0.02   0.88      0.18\n",
      "race km              1.44   0.23      2.12\n",
      "     rank            1.46   0.23      2.14\n",
      "wexp km              7.48   0.01      7.32\n",
      "     rank            7.18   0.01      7.08\n",
      "\n",
      "\n",
      "1. Variable 'age' failed the non-proportional test: p-value is 0.0009.\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",
      "2. Variable 'wexp' failed the non-proportional test: p-value is 0.0063.\n",
      "\n",
      "   Advice: with so few unique values (only 2), you can include `strata=['wexp', ...]` in the call in\n",
      "`.fit`. See documentation in link [E] below.\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"
     ]
    },
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 424
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 424
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.check_assumptions(rossi, p_value_threshold=0.05, show_plots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can use the proportional hazard test outside of `check_assumptions`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.StatisticalResult>\n",
      "         test_name = proportional_hazard_test\n",
      "    time_transform = rank\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 1\n",
      "             model = untransformed variables\n",
      "\n",
      "---\n",
      "      test_statistic     p  -log2(p)\n",
      "age           11.094 0.001    10.173\n",
      "fin            0.017 0.896     0.158\n",
      "mar            0.666 0.414     1.271\n",
      "paro           0.138 0.711     0.493\n",
      "prio           0.023 0.881     0.183\n",
      "race           1.462 0.227     2.141\n",
      "wexp           7.180 0.007     7.084\n"
     ]
    }
   ],
   "source": [
    "from lifelines.statistics import proportional_hazard_test\n",
    "\n",
    "results = proportional_hazard_test(cph, rossi, time_transform='rank')\n",
    "results.print_summary(decimals=3, model=\"untransformed variables\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stratification\n",
    "\n",
    "\n",
    "In the advice above, we can see that `wexp` has small cardinality, so we can easily fix that by specifying it in the `strata`. What does the `strata` do? Let's go back to the proportional hazard assumption.\n",
    "\n",
    "In the introduction, we said that the proportional hazard assumption was that \n",
    "\n",
    "$$ h_i(t) = a_i h(t)$$\n",
    "\n",
    "In a simple case, it may be that there are two subgroups that have _very_ different baseline hazards. That is, we can split the dataset into subsamples based on some variable (we call this the stratifying variable), run the Cox model on all subsamples, and compare their baseline hazards. If these baseline hazards are _very_ different, then clearly the formula above is wrong - the $h(t)$ is some weighted average of the subgroups' baseline hazards. This ill fitting average baseline can cause $a_i$ to have time-dependent influence. A better model might be:\n",
    "\n",
    "$$ h_{i |i\\in G}(t) = a_i h_G(t)$$\n",
    "\n",
    "where now we have a unique baseline hazard _per_ subgroup $G$. Because of the way the Cox model is designed, inference of the coefficients is identical (expect now there are more baseline hazards, and no variation of the stratifying variable within a subgroup $G$). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  \n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'week'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strata</th>\n",
       "      <td>[wexp]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-580.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2019-11-17 14:00:08 UTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>wexp in strata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>0.05</td>\n",
       "      <td>4.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-2.64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>6.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.31</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2.49</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.34</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.35</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3.16</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.fit(rossi, 'week', 'arrest', strata=['wexp'])\n",
    "cph.print_summary(model=\"wexp in strata\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ``p_value_threshold`` is set at 0.01. 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",
      "<lifelines.StatisticalResult>\n",
      "         test_name = proportional_hazard_test\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 1\n",
      "\n",
      "---\n",
      "           test_statistic      p  -log2(p)\n",
      "age  km             11.29 <0.005     10.32\n",
      "     rank            4.62   0.03      4.99\n",
      "fin  km              0.02   0.90      0.16\n",
      "     rank            0.05   0.83      0.28\n",
      "mar  km              0.53   0.47      1.10\n",
      "     rank            1.31   0.25      1.99\n",
      "paro km              0.09   0.76      0.40\n",
      "     rank            0.00   0.97      0.05\n",
      "prio km              0.02   0.89      0.16\n",
      "     rank            0.02   0.90      0.16\n",
      "race km              1.47   0.23      2.15\n",
      "     rank            0.64   0.42      1.23\n",
      "\n",
      "\n",
      "1. Variable 'age' failed the non-proportional test: p-value is 0.0008.\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"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 424
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.check_assumptions(rossi, show_plots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `age` is still violating the proportional hazard assumption, we need to model it better.  From the residual plots above, we can see a the effect of age start to become negative over time. This will be relevant later. Below, we present three options to handle `age`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modify the functional form\n",
    "\n",
    "The proportional hazard test is very sensitive (i.e. lots of false positives) when the functional form of a variable is incorrect. For example, if the association between a covariate and the log-hazard is non-linear, but the model has only a linear term included, then the proportional hazard test can raise a false positive. \n",
    "\n",
    "The modeller can choose to add quadratic or cubic terms, i.e:\n",
    "```\n",
    "rossi['age**2'] = (rossi['age'] - rossi['age'].mean())**2\n",
    "rossi['age**3'] = (rossi['age'] - rossi['age'].mean())**3\n",
    "```\n",
    "\n",
    "but I think a more correct way to include non-linear terms is to use splines. Both Patsy and zEpid provide functionality for splines (tutorial incoming), but let's stick with the form above. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  \n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'week'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strata</th>\n",
       "      <td>[wexp]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-579.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2019-11-17 14:00:10 UTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>quad and cubic age terms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<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>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>-0.37</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.05</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-1.85</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.35</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>-0.39</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.43</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.33</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3.22</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>9.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age**2</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age**3</th>\n",
       "      <td>-0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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    },
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     "output_type": "stream",
     "text": [
      "\n",
      "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",
      "<lifelines.StatisticalResult>\n",
      "         test_name = proportional_hazard_test\n",
      " null_distribution = chi squared\n",
      "degrees_of_freedom = 1\n",
      "\n",
      "---\n",
      "             test_statistic    p  -log2(p)\n",
      "age    km              0.96 0.33      1.62\n",
      "       rank            4.09 0.04      4.54\n",
      "age**2 km              1.81 0.18      2.48\n",
      "       rank            0.79 0.37      1.42\n",
      "age**3 km              2.33 0.13      2.98\n",
      "       rank            0.03 0.87      0.19\n",
      "fin    km              0.03 0.87      0.20\n",
      "       rank            0.02 0.90      0.15\n",
      "mar    km              0.53 0.47      1.10\n",
      "       rank            0.94 0.33      1.59\n",
      "paro   km              0.20 0.66      0.60\n",
      "       rank            0.01 0.93      0.10\n",
      "prio   km              0.02 0.88      0.19\n",
      "       rank            0.01 0.90      0.15\n",
      "race   km              1.28 0.26      1.96\n",
      "       rank            0.47 0.49      1.02\n",
      "\n",
      "\n",
      "1. Variable 'age' failed the non-proportional test: p-value is 0.0431.\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"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 284,
       "width": 424
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rossi_higher_order_age = rossi.copy()\n",
    "rossi_higher_order_age['age'] = rossi_higher_order_age['age'] - rossi_higher_order_age['age'].mean()\n",
    "rossi_higher_order_age['age**2'] = (rossi_higher_order_age['age'] - rossi_higher_order_age['age'].mean())**2\n",
    "rossi_higher_order_age['age**3'] = (rossi_higher_order_age['age'] - rossi_higher_order_age['age'].mean())**3\n",
    "\n",
    "cph.fit(rossi_higher_order_age, 'week', 'arrest', strata=['wexp'])\n",
    "cph.print_summary(model=\"quad and cubic age terms\"); print()\n",
    "cph.check_assumptions(rossi_higher_order_age, show_plots=True, p_value_threshold=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see we still have potentially _some_ violation, but it's a heck of a lot less. Also, interestingly, when we include these non-linear terms for `age`, the `wexp` proportionality violation disappears. It is not uncommon to see changing the functional form of one variable effects other's proportional tests, usually positively. So, we could remove the `strata=['wexp']` if we wished. \n",
    "\n",
    "#### Bin variable and stratify on it\n",
    "\n",
    "\n",
    "The second option proposed is to bin the variable into equal-sized bins, and stratify like we did with `wexp`. There is a trade off here between estimation and information-loss. If we have large bins, we will lose information (since different values are now binned together), but we need to estimate less new baseline hazards. On the other hand, with tiny bins, we allow the `age` data to have the most \"wiggle room\", but must compute many baseline hazards each of which has a smaller sample size. Like most things, the optimial value is somewhere inbetween."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>age_strata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>(24, 27]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>(15, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>(18, 21]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>(21, 24]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>(18, 21]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age age_strata\n",
       "0   27   (24, 27]\n",
       "1   18   (15, 18]\n",
       "2   19   (18, 21]\n",
       "3   23   (21, 24]\n",
       "4   19   (18, 21]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rossi_strata_age = rossi.copy()\n",
    "rossi_strata_age['age_strata'] = pd.cut(rossi_strata_age['age'], np.arange(0, 80, 3))\n",
    "\n",
    "rossi_strata_age[['age', 'age_strata']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxPHFitter:\"None\", fitted with 432 total observations, 318 right-censored observations>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the orignal, redundant, age column\n",
    "rossi_strata_age = rossi_strata_age.drop('age', axis=1)\n",
    "cph.fit(rossi_strata_age, 'week', 'arrest', strata=['age_strata', 'wexp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  \n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'week'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strata</th>\n",
       "      <td>[age_strata, wexp]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-392.443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2019-11-17 14:00:13 UTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>stratified age and wexp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
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    },
    {
     "data": {
      "text/html": [
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       "  <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>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>-0.395</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.197</td>\n",
       "      <td>-0.781</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.991</td>\n",
       "      <td>-2.004</td>\n",
       "      <td>0.045</td>\n",
       "      <td>4.472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.280</td>\n",
       "      <td>1.324</td>\n",
       "      <td>0.313</td>\n",
       "      <td>-0.334</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.716</td>\n",
       "      <td>2.447</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.371</td>\n",
       "      <td>1.431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>-0.194</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.392</td>\n",
       "      <td>-0.961</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.382</td>\n",
       "      <td>1.776</td>\n",
       "      <td>-0.494</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>-0.163</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.200</td>\n",
       "      <td>-0.555</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.574</td>\n",
       "      <td>1.256</td>\n",
       "      <td>-0.818</td>\n",
       "      <td>0.413</td>\n",
       "      <td>1.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>0.080</td>\n",
       "      <td>1.084</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.135</td>\n",
       "      <td>1.025</td>\n",
       "      <td>1.145</td>\n",
       "      <td>2.854</td>\n",
       "      <td>0.004</td>\n",
       "      <td>7.857</td>\n",
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    {
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       "<matplotlib.axes._subplots.AxesSubplot at 0x105873d30>"
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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JkiSpAQz2kiRJUgMY7CVJkqQGMNhLkiRJDWCwlyRJkhrAB1RJkirnA6okaesM9pKkys3NzVVdgiTVnk1xJEmSpAYw2EuSJEkNYLCXJFVuamqKqampqsuQpFoz2EuSJEkNYLCXJEmSGsBgL0mSJDWAwV6SJElqAIO9JEmS1AAGe0mSJKkBfPKsJKlyMzMzVZcgSbVnsJckVW5ycrLqEiSp9myKI0mSJDWAwV6SVLlWq0Wr1aq6DEmqNZviSJIqNz8/D9gkR5K2wiv2kiRJUgMY7CVJkqQGMNhLkiRJDWCwlyRJkhrAYC9JkiQ1gMFekiRJagC7u5QkVW5xcbHqEiSp9rxiL0mSJDWAwV6SJElqAIO9JKlys7OzzM7OVl2GJNWabewlSZVbWVmpugRJqj2v2EuSJEkNYLCXJEmSGsBgL0mSJDWAwV6SJElqAIO9JEmS1AD2iiNJqtzk5GTVJUhS7RnsJUmVm5mZqboESao9m+JIkiRJDWCwlyRVbmVlxYdUSdIW2RRHklS52dlZABYXFyuuRJLqyyv2kiRJUgMY7CVJkqQGMNhLkiRJDWCwlyRJkhrAYC9JkiQ1gMFekiRJagC7u5SkEbR7926Wl5c3vNz4+Dh79uwZQkXDNTc3V3UJklR7BnupZhYWFti/fz9jY2NMT09XXY6GZHl5maWlparL2DY7d+6suoTa8DNAUi8Ge6lmFhYWWFpaYteuXf5SPwjs2LGD8fHxdedbXl7mwIED21CRquZngKReDPaSNMLGx8fZu3fvuvNNTEzU+gr//Pw8ADMzMxVXIkn1teWbZyNiLCIyIhYi4uiI+MeIuCQifhARn4qIh3XMvyMiXhwRrYj4RkRcExHfiYh/ioj79thGRsTeiLhtRMxHxDcj4scRMd02z+0i4q0Rsb9tnR+MiOO2uo+SpOFqtVq0Wq2qy5CkWhvkFfudwL8BFwCnArcDngycHhFTmXlaOd8xwBzwSeD/B74P3Al4DHBCRDw6Mz/WZf1HAv8OXAF8ELgOuAggInYCnwKOAlrAe4E7Ar8JPDIinpiZHx3gvkqSJEkjZZDB/kHAX2Tmi1dHRMRfUYT9UyLi9My8DNgHHJWZ321fOCLuAHwaeCPQLdj/MvAu4BmZeW3HtFMoQv3LMvMnXStExF9TfIF4Z0TcOTOvWGsHIuK8HpOOXms5qQrLy8tMTExUXYaGZDM94qwuV8fzYt++fQC1rH27bfbckNR8gwz2B4BXt4/IzHMj4j3AicDjgXdmZte7uzLzGxHxfuC5EXGnzPxaxyzXAH/UGerLLwQPA74GvL5jnedExHuBpwJPAP5u03snjZgDBw7Uuk21hqPu58XFF19cdQmSVFuDDPafzczLu4zfSxHs7w28EyAi7g88H7gv8PPAjTuWuT1FUG+3PzO7feLfuxyelZk/6jK9RRHs7806wT4zu7bHL6/kH7vWstJ267e3FNXTZnu5qet5sXrF/phjjqm4ktFnD0iSehlksL+ox/hvl8MdABHxeOD9wFXAGcBXgB9QtJmfAHYBN1ljPZ12lMMLe0xfHX9Ej+lSLfXbW4rqabO93NT1vJiamgJgcXGx4kpGX917QJI0PIMM9rfpMf625XD18sJrKJrV3Ccz97XPGBGnUgT7brLH+NX13rbH9Nt1zCdJGjE+oEqStm6Qwf7YiDisS3OciXJ4fjm8G/CfXUL9IcADNrHd1fU+ICJu2OXG2geXw89uYt2SpG0wNze3/kySpDUNMtjvAF4BtPeKcx/gKRRXyz9Ujt4P/EJEHJWZ3yrnC+Bk4J4b3Wh50+0ZwG8Au4G/aNv+8cAURZeaH+q+BkkaXf32cmNPKZKkQQb7TwIzZZg+m5/2Y38IcFLZ1SUU3VmeApwfER8AfgTcnyLUfwR49Ca2/axym39ePhDrXH7aj/11wNN73Ngr1c709DQTExOMjY1VXYq2Qd17udHg+RkgqZdBBvsVioD9unJ4E4rmL6/OzI+vzpSZp0bE1RRX108ErgTOAp4OPJFNBPvM/Gr514GXAY+gaP5zGUV/+HOZ+ZnN75Y0Wqanp6suQdtgsz3b1LFHHPDm2Y3wM0BSL4MM9pTt5h/bx3wLwEKXSRdQNMnpnD/6WOc3gT9Ybz5JqoM9e/ZUXYIkqWYOqboASZIkSVtnsJckSZIawGAvSZIkNcCW29hn5n5g3TbwkiRJkobHK/aSJElSAwy0VxxJkjZjZmam6hIkqfYM9pKkyk1OTlZdgiTVnk1xJEmSpAYw2EuSKtdqtWi1WlWXIUm1ZlMcSVLl5ufnAZvkSNJWeMVekiRJagCDvSRJktQABntJkiSpAQz2kiRJUgMY7CVJkqQGMNhLkiRJDWB3l5Kkyi0uLlZdgiTVnlfsJUmSpAYw2EuSJEkNYLCXJFVudnaW2dnZqsuQpFqzjb0kqXIrKytVlyBJtecVe0mSJKkBDPaSJElSAxjsJUmSpAYw2EuSJEkNYLCXJEmSGsBecSRJlZucnKy6BEmqPYO9JKlyMzMzVZcgSbVnUxxJkiSpAQz2kqTKrays+JAqSdoim+JIkio3OzsLwOLiYsWVSFJ9ecVekiRJagCDvSRJktQABntJkiSpAQz2kiRJUgMY7CVJkqQGMNhLkiRJDWB3l5Kkys3NzVVdgiTVnsFeklS5nTt3Vl2CJNWeTXEkSZKkBjDYS5IqNz8/z/z8fNVlSFKtGewlSZVrtVq0Wq2qy5CkWjPYS5IkSQ1gsJckSZIawGAvSZIkNYDBXpIkSWoAg70kSZLUAD6gSpJUOR9QJUlbZ7CXJFVubm6u6hIkqfZsiiNJkiQ1gMFekiRJagCDvSSpclNTU0xNTVVdhiTVmsFekiRJagCDvSRJktQABntJkiSpAQz2kiRJUgMY7CVJkqQGMNhLkiRJDeCTZyVJlZuZmam6BEmqPYO9JKlyk5OTVZcgSbVnUxxJkiSpAQz2kqTKtVotWq1W1WVIUq3ZFEeSVLn5+XnAJjmStBVesZckSZIawGAvSZIkNYDBXpIkSWoAg70kSZLUAAZ7SZIkqQEM9pIkSVID2N2lJKlyi4uLVZcgSbXnFXtJkiSpAQz2kiRJUgMY7CVJlZudnWV2drbqMiSp1mxjL0mq3MrKStUlSFLtecVekiRJagCDvSRJktQABntJkiSpAQz2kiRJUgMY7CVJkqQGsFccSVLlJicnqy5BkmrPYC9JqtzMzEzVJUhS7dkUR5IkSWoAg70kqXIrKys+pEqStsimOJK0jXbv3s3y8vKGlxsfH2fPnj1DqGg0zM7OArC4uFhxJZJUXwb7EbewsMD+/fsZGxtjenq66nIkbdHy8jJLS0tVlyGpQv5u17AY7EfcwsICS0tL7Nq1yze/1CA7duxgfHx83fmWl5c5cODANlQkabv4u13DYrCXpAqMj4+zd+/edeebmJjwCr8kqS9Du3k2IsYiIiNiISLuGhHvj4jvRcTlEfGJiPilcr5bR8TbIuLCiLgqIj4TEQ/uWNdREfGKiDg7Ir4dEddExLciYjEi7rnOtu8eEadFxMURcV1ETAxrnyVJkqSqbMcV+zHgP4B9wEL58+OBvRFxX+BjwGXAacCRwG8Dp0fE3TPza+U6HgS8BDgT+ABwBfALwJOAx0TE/TPzc122fddy218C3gMcWm5LkiRJapTtCPa7gJdl5tzqiIh4OfBqitD998AfZuZ15bQzgL8DXlC+AFrAbTLz8vYVR8S9gLOB1wEndNn2A4DXZuZL+yk0Is7rMenofpYfpuXlZSYmJqouQ9IWbaZHnNXlmvwZsG/fPoBG76O0arOfA9J6tiPY76cI3u3eSRHsbwK8eDXUlxaBtwM/uassMy/utuLM/FxEtICHRcSNMvNHHbNcBLxqa+WPhgMHDtjOVjqIHSyfARdf3PXjXpLUh+0I9suZ+eOOcd8qh1/qvAqfmT+OiIuAO7SPj4hHAs8C7gPciuvXfivgwo5xn8vMq/stNDOP6za+vJJ/bL/rGYZ+e9CQNNo228uNnwFSc9jblYZlO4L99c7czLw2IrpOK10L3Gj1h4h4PrAH+D5wBvA14IdAAo8D7kVx9b/Tt7dS+CjptwcNSaNts73c+BkgNYe9XWlYRr67y4i4IXAyRUg/NjMv7Jh+3zUWzyGWJkkakPn5eQBmZmYqrkSS6mto3V0O0K2AI4BzuoT6n6PiJjKSpK1rtVq0Wq2qy5CkWhv5K/bAxRTNbo6LiJ/LzCsAIuJGwJsogr8k1Uq/vdzYe4YkqV8jH+wz87qIeDNFP/YXRMSHgRsDD6bo9/7M8t+NND09zcTEBGNjY1WXImmADpZebiRdn7/bNSwjH+xLLwe+A8wAJ1HcdHsG8DIa0p1lL9PT01WXIGmANtuzjT3iSM3h73YNy9CCfWbuB2KN6WtNG+v4+VrgDeWr03T56nvbklSVPXv2VF2CJKmh6nDzrCRJkqR11KUpjiSpwXbu3Fl1CZJUewZ7SVLl5ubmqi5BkmrPpjiSJElSAxjsJUmSpAYw2EuSKjc1NcXU1FTVZUhSrRnsJUmSpAYw2EuSJEkNYLCXJEmSGsBgL0mSJDWAwV6SJElqAIO9JEmS1AA+eVaSVLmZmZmqS5Ck2jPYS5IqNzk5WXUJklR7NsWRJEmSGsBgL0mqXKvVotVqVV2GJNWaTXEkSZWbn58HbJIjSVvhFXtJkiSpAQz2kiRJUgMY7CVJkqQGMNhLkiRJDWCwlyRJkhrAYC9JkiQ1gN1dSpIqt7i4WHUJklR7XrGXJEmSGsBgL0mSJDWAwV6SVLnZ2VlmZ2erLkOSas029pKkyq2srFRdgiTVnlfsJUmSpAYw2EuSJEkNYLCXJEmSGsBgL0mSJDWAwV6SJElqAHvFkSRVbnJysuoSJKn2DPaSpMrNzMxUXYIk1Z5NcSRJkqQGMNhLkiq3srLiQ6okaYtsiiNJqtzs7CwAi4uLFVciSfXlFXtJkiSpAQz2kiRJUgMY7CVJkqQGMNhLkiRJDWCwlyRJkhrAYC9JkiQ1gN1dSpIqNzc3V3UJklR7BntJUuV27txZdQmSVHs2xZEkSZIawGAvSarc/Pw88/PzVZchSbVmsJckVa7VatFqtaouQ5JqzWAvSZIkNYDBXpIkSWoAg70kSZLUAAZ7SZIkqQEM9pIkSVID+IAqSVLlfECVJG2dwV6SVLm5ubmqS5Ck2rMpjiRJktQABntJkiSpAQz2kqTKTU1NMTU1VXUZklRrBntJkiSpAQz2kiRJUgMY7CVJkqQGMNhLkiRJDWCwlyRJkhrAYC9JkiQ1gE+elSRVbmZmpuoSJKn2DPaSpMpNTk5WXYIk1Z5NcSRJkqQGMNhLkirXarVotVpVlyFJtWZTHElS5ebn5wGb5EjSVnjFXpIkSWoAg70kSZLUAAZ7SZIkqQEM9pIkSVIDGOwlSZKkBjDYS5IkSQ1gd5eSpMotLi5WXYIk1Z5X7CVJkqQGMNhLkiRJDWCwlyRVbnZ2ltnZ2arLkKRas429JKlyKysrVZcgSbXnFXtJkiSpAQz2kiRJUgOMbFOciHge8CxgJ3BT4AXAG4GlzJyosDRJm7B7926Wl5c3vNz4+Dh79uwZQkWSJDXLSAb7iPht4E3A+cAe4Grg3ystSkO3sLDA/v37GRsbY3p6uupyNGDLy8ssLS1VXYZGSPt7XpK0dSMZ7IFHrQ4z81urIyPiGOCH1ZSkYVtYWGBpaYldu3YZ7Btsx44djI+Przvf8vIyBw4c2IaKVJX29/xRRx1VdTmSVHujGuyPAmgP9eXPX6imHEmDMj4+zt69e9edb2Jiwiv8B5HJycmqS5Ck2hupm2cj4uSISODB5c+5+mr7eW+3ZSJiIiKeFBGfjogfRsQlEfG+iLj99u+JJGkjZmZmmJmZqboMSaq1Ubtiv7ccTgN3Bl61gWX/EHgM8E/AEnA88GTgXhExnplXD65MSZIkabSMVLDPzL3A3oiYAO6cmSdvYPGHA7+amResjoiIReB3gMcCf7/eCiLivB6Tjt5AHdqi5eVlJiYmqi5DA7aZHnFWl/N8aKb2c2L1AVU7d+6sqhxJqr2RCvZb9Ob2UF/6G4pg/2v0Eew1Gg4cOGDbav2E58PBYXZ2FoDFxcWKK5Gk+mpSsD+3y1I9hPkAABgtSURBVLivl8Nb9LOCzDyu2/jySv6xm6xLG9Rvrymql832cuP50Fz2fCRJg9WkYH9pl3HXlsMbbGch2pp+e01RvWy2lxvPh+ay5yNJGqyR6hVHkiRJ0uYY7CVJkqQGaFJTHEk10G8vN5vtRUeSpIOVwV4jY3p6momJCcbGxqouRUNkLzda1f6e/8QnPlF1OZJUewZ7jYzp6emqS9AQbbZnG3vEaa729/yuXbuqK0SSGmIkg31mTvQYH13GnQyc3GP+/cD1lpG0/fbs2VN1CRphPphKkrbOm2clSZKkBjDYS5IqNz8/z/z8fNVlSFKtGewlSZVrtVq0Wq2qy5CkWjPYS5IkSQ1gsJckSZIawGAvSZIkNYDBXpIkSWoAg70kSZLUACP5gCpJ0sHFB1RJ0tYZ7CVJlZubm6u6BEmqvcjMqmsYeRHxvUMPPfTIY445pupSJEmS1GD79u3jyiuvvCQzb7nRZQ32fYiIFeBwYP8WV3V0OfzCFtdzsPM4DobHcTA8joPhcRwMj+NgeBwHw+O4OWPAZZm54TaKBvttFBHnAWTmcVXXUmcex8HwOA6Gx3EwPI6D4XEcDI/jYHgct5+94kiSJEkNYLCXJEmSGsBgL0mSJDWAwV6SJElqAIO9JEmS1AD2iiNJkiQ1gFfsJUmSpAYw2EuSJEkNYLCXJEmSGsBgL0mSJDWAwV6SJElqAIO9JEmS1AAGe0mSJKkBDPabFBE3iojnR8Q7ImI5Iq6JiIyImS2s834R8c8RcUlEXBkR/zcidkfEDdZY5lERsTciDkTEFRHxHxFx4mZrqMpm9r3LOk4u/w/Wen2lY5mJdeZ/3eD3dngGcRzL9ax1TP59jeU8H3+6jttHxHMj4vSI2B8RV0fE9yLijIh4Qo9lanc+RsQdIuLtEfGtch/3R8SeiLjFBtdzZLnc6rH6VrneOwx726Ngq/sSETePiKdExGJEfCEifhARl0fEuRHxooi4cY/lNvVeH1WDOCfKz7C1jstNeyx3z4j4+4i4OCKuiogvRsSrIuLQwe3h9hjA+bjeZ9nq644dyzXqfKzCDasuoMZuDuwp/30R8G3gjr1nX1tEPBb4AHAVcBpwCfBo4I3A/YHf7LLMc4C3AN8D3g1cAzwJWIiIX87MP9psPdtpM/vew941pj0aOBY4vcf0pR7Lf6rPbVdugMdx1f8AC13Gf6PH9j0ff9ZzgT8GVoAzKT4j7gw8AXhoRLwxM1/YY9lanI8RcVfgHODngQ8DXwB+DXg+8PCIuH9mfq+P9dyyXM/dgRbwPuBo4OnAIyPivpn51WFsexQMaF8eSPG+u4TifPtH4BbAY4C/AJ4QEQ/JzKu6LLuh9/qoGsI58aoe46/tsu3jKc7dGwHvB74OTAKvAB5SHvurN7DtygzoOO6n9/H7ZYrPwc9n5te7TG/E+ViZzPS1iRdwY+AE4HblzycDCcxsYl2HAxcDVwP3aRt/U4o3VwK/3bHMGEXw+B4w1jb+FsB/l8vct+rjNIx938Q2bkDxIZvAr3RMmyjHn1z1sRil41jOv3cD83s+Xn9dTwB2dRl/DHCgXNdxHdNqdT4CHy/rfW7H+DeU40/pcz2nlvP/Zcf455XjPzasbY/CaxD7AowDTwFu3DH+MOC8cj0v6rLcht7ro/wa4Pm4F8gNbPcGwH+V23hM2/hDKEJ+Ai+p+vhs93FcY/3vLdfzvC7TGnM+Vvb/V3UBTXmxtWD/jHLZd3aZNllOW+oY/+py/Ks2sr5Re21m3zexjUeX6/m3LtNqFaS26zhu9MPV83HD23lbt6BVp/MRuGtZ6wpwSMe0w4ArgB8AN19nPT8H/LCc/7COaYdQXPlL4C6D3vYovLZjX4Cpchsf6TKtEUFqkMeRjQf7np8NwF3KafuBqPo4bedx7LH+W1FcBPohcESX6Y04H6t82cZ+NEyWw491mfZJijfA/SLiJn0uc3rHPKNsM/u+Ub9fDt+2xjx3i4jnRMRLI+IZEfELW9heFYZxHI8oj8VLI+LZEfHrm9y+5+P1/agcXu9P+qU6nI8PLoefyMzr2idk5uXA2cDNgLXOG8rphwJnl8u1r+c6iquH7dsb5LZHwXbsy3rn20be66Nq4McxIp4cES+JiBdGxAlrvO97fm5k0YTsSxTN8O7S77YrNOzz8UTgJsA/ZOalPeZpwvlYGdvYj4Z7lMMvdU7IzGsjYgX4RYoPhX19LHNhRPwAuENE3CwzfziEmgdlM/vetyhuvDuBounDaWvM+pTy1b7sB4BnZub3N7rdCgzjON4L+Nv2ERHxOeBpmXnBBrbv+dgmIg4HnkhxZeoTPWarw/nY81iVvgw8jKLd/L9ucT2U6xn0tkfBduzLM8phty+ssLH3+qgaxnF8X8fPF0fEszPz/ZvY9t3L11d6zDMqhn0+PrMcnrrGPE04HyvjFfvRsKMcHugxfXX8EZtYZkeP6aNiM/u+Eb9H0f7x3T0C5XeAl1DczHMYcGuKLwLnU4Svj0REHd4ngz6Ob6C4UfTWFMflVynait4LaEXE7Te5/YP6fIyIAOaB2wD/JzM7vxzU6Xwc1LEa5uffZj83ttOwz7nnAA8HloG3d5llo+/1UTXI4/hhiiacd6D4a9LRwGvLZU+LiIcPcdtVG9q+RMQuii8On8/Mc3rM1pTzsTKj8guiEmX3Tf10x7T6enfVNY+iUT2OZQD6vfLHrlcHMvM/M/PPMvPzmXlFZn43Mz9G0dZ5heID5tHbVO/IHMfMfFFmnlMejysy89zM/E2K3mJuBYxsDzejdBy7+EuKXnXOAq7XI84onY+qvyi6Vd1D0SPTEzPzR53z1Pm9PiyZ+cbM/GhmfjMzr8rML2bmS4EXUeSm11ZcYl2t2yzW83HrDvamOF+huImjX98aUh3rXc1cHd/eHu0AxUm+g6Inkl7L9PrWPUhbOY6b2fd+nUDRBem/b/TPd5l5WUQsArPAgyiu4AzbqB7HdqdQXDl+UMd4z8d1RMTrgRdQtNV/ZG6g67uKzsf1DOpYbfbzbxDbHgVD2ZeIeBxFU5KLgQdnR3ehfej1Xh9V23FOzFN0ezseEYe13RPi+biOiDiS4ny6EnjXJuqq2/lYmYM62GfmQ6quofRF4D4UbdbOa58QETcEdlLc9PTVjmVuVS7zbx3L3I6in/1vbEd75i0ex83se79Wrw6s1ZZvLd8phzff5PIbMsLHsV2vY+L5uIaIeCOwm6J/8Udt8jhs6/nYhy+Ww7v3mL56w2+vtrpbWc+gtj0KBr4vEfGbwCLFlfrJzPzyOot0M2rn23qGfk5k5lURcTlFN743B1aDvefj+lZvmn3nGjfNrqVu52NlDuqmOCOkVQ472+1B8e30ZsA5HVf41lrmhI55Rtlm9n1dEXEU8EjWv2l2Lat34m81DG+HoRzHLnodE8/HLqLwVopQfwbFlfrNfrkZtfPxzHL4sM52/xFxGEWzoR8C6z0t8t8pruLdv1yufT2HUNyo1769QW57FAx0XyLiKRT9hH+L4jkKmwn1MHrn23qGfk5ExD0oQv3lwHfbJvX83IiIu1CE5P+hHsdyWMdx9abZtXqnW0vdzsfqVN3fZlNe9NGPPcWfsI6mfKhV2/jDKb6NbuQBVTtpzgOBNrrvNyuP453WWO/Ly2Xfss7279Nj/FOB68q6xvrZl6YcR+BXgBt12cavUPwyS2DK83Hd4xjA35TL/DNw0z62X6vzkQ0+yKY8Tkd3WY8PqBrMcTwR+DFF+LlzH9vd8Ht9lF+DOI7lZ9mRXdZ967bPgLd1TFvrAVX/QMMfUNXrfGyb/sByuQsOpvOxqleUB02bEBEvoTihoXjq370o3virV0g+lZnzbfNPA++g+FPUdMe6Hkdx5/dVFO0iL6F4FPg9yvG/lR3/WRHxXODNFGHqNOAa4EkUd/L/ZWbW4iaTje57RExQXFVYysyJLus7hPIXG8WTZnu2r4+I/RRNK86leFz1TSnuwv+1cvwzM3Nhi7u4LQZ1HCNigeIGzbMonth7NcV5/nCKX2B/A5zk+fiT+SfofhxfSfGF/0qKGxiv6bK55cz8x7Zl9lOj8zGu/+j5fcDxFH1hfwm4X7Y9ej4iiifQZEbHem5ZrufuFFc/P03xhN7HUrQRv19mfqVjmQ1te5QN4jhGxIOBf6EIk2+neO92ujQz97Qts8Am3uujakDHcZqiPfenKH6PXALcCXgExcW5c4HfyI7mJBFxPMW5eyOKz4mvAQ+haNp3NvCQ3PpfTLfFoN7XbdPfRXFx4nmZ+ZY1trtAg87HylT9zaLOL8qn063xWuiYf7rb+Lbp96e4svd9ijBwAcXNdjdYo4ZHA0sUfxr8AfAZ4MSqj80mjmXf+85Pn865t8e6TqDHk2a7zPvHFE0kvl5u9yqKmy/fAdyr6uNSxXEEHgd8kOJK+2UUgfRC4CO0XY3yfFz3OC6s8/nQ7TOiducjxQ3q7yjPkWsomhzsAW7RZd6kxxM9gSOBN5XLr55zbwfuMIhtj/prq8eRn/5+Weu1v2OZTb/XR/U1gOP4y+V79wKKixQ/ogj3ZwHPBW68xrbvSXGF/rsUofRLwKuAQ6s+Ltt9HNum3aL8LOv6pNmmn49VvLxiL0mSJDWAN89KkiRJDWCwlyRJkhrAYC9JkiQ1gMFekiRJagCDvSRJktQABntJkiSpAQz2kiRJUgMY7CVJkqQGMNhLkiRJDWCwlyRJkhrAYC9JkiQ1gMFekjpExFhEZEQsVFjD3SPimoj4X1XVUNYREfG5iDhrC+sYiX2pi4i4WUR8OyLeXXUtkurFYC9Jo+kNwPeAv2ofGREL5ZeO6V4LRsTJ5Twn9xjf/roqIv47It4WEWOd68rMBF4BPCAinjTIfSlrOjYi/iEiLirD/9ci4q8j4jY99m1/l31YfX27y/w3jog/jYiViDgQEWdGxLE91v3Qcj2P2uR+EhG3jIiXR8Q5EfHdiPhRRHwvIs6KiJd27ldE7C23ObE6LjN/CLwWmIqIX91sLZIOPjesugBJ0s+KiPsBjwRmy5A3aEvA3vLftwQmgWcCT4qI4zPzy+0zZ+aHI2IfMBcRHyjDfl/W2pcyQH+Q4nfRR4AvAUcDzwIeHRH3z8yvdVntAWBPl/FXdBn3OuAFwAeAbwBPA86MiKMz88K2Wn4O+BvgPZn50X73r8v+vBvYAfw38CHg4vLn44E/BV4aEXfLzOt9CelwKvBKYA542GbqkXTwMdhL0uh5NnAd8HdDWv/ezDx59YeIOIQiWD8CeCnw9C7LvJMiJD8E+JcNbKvrvkTETYF54EbAEzPzg23TfgdYpLjC/5gu67y0vf5eIiKAk4B3ZOYzynEfovhS8zTg9W2zvw44FHh+n/vVua1dFEH+Worj987OL0AR8cvAm4Cbrre+zLwqIk4DToqIX+j8siVJ3dgUR5I2ICJuFxFvLZuEXBMR34mID0bEcT3m3xEReyLiG2Wzly9ExAsj4i7d2vFHxOHAk4BzMvMb27BLZOZ1wGodvZp+vK8c/l6/611nX+4H3AY4tz3Ul/W8F/gc8KiIuHO/2+vi1sDNgE+3jVv990/WGxEPBP4QeE5mfm+jGym/GJ1KcbHs+Zm50O2vGpl5AfBQ4Jt9rvp9QADP2GhNkg5OXrGXpD5FxE7gU8BRQAt4L3BH4DeBR0bEE9ubcZRXpVvAscD5wHsommXMAg/ssZkHATcut1OFH3UbmZn/ExHfBB4aEdFnc5y19uW25fCrPZb9KnAvimZC7+iYdpOIeCpwJ+AHwP8FPpmZP+6Y77vAlUD7l677lMP/AYiIQ4G/BT6Yme9fb4d62AXcgyKw/+1aM5Zfoq7rc72fpvj/+A3gTzZZm6SDiMFekvp3CkWof1lmzq2OjIi/Bj4JvDMi7pyZq229X0wR6t8HTK2G4YiYAz7bYxsPKIfnrlPL47rd7FqaWGfZnxERN+CnV+LX+kLxGeBxwDHAf/Wx6rX25bvlcGePZe9SDu/RZdptgXd1jFuJiKdn5tLqiMy8LiLeBjwvInZQBO+nAZdRfMkCeA3FfQbPXmtH1rG6n3u7fLnYtMy8MiL+E7h3RByWmZcPat2SmslgL0l9iIg7UNzE+DV+tm02mXlORLwXeCrwBH7anvxEiquzf9J+hTszvx4Reyhupux0p3J4YZdp7R5bvjZjoq3HnCMprggfTRHWX7PGcqs3fN6J/oL9WvtyNnAp8KsR8djM/PDqhIj4LYqr9QC36FjuHcBZwH8Cl1N8AXgO8PvA6RFx38z8XNv8f0xx1f53KPb1fOCPMvObEfFrwG5gGvhOeUxOomjC8zngeZl5dh/7ebtyOIymU98GxoHbA18YwvolNYht7CWpP/cuh2dlZrfmKq32+cr25XcFvpmZ+7vM3+vK+C3L4ffXqefpmRndXsCr1ll2F0WPK68EnksR6peB+2XmxWssd0k5vNU661/Vc18y8wcUN6om8MGI+FBEvD4i/oniLxzL5azXdSz3qsxsZeZFmfnDzPx8Zj6LokvNQ4GTO+a/OjP/JDPHMvPwzNyVmZ+JiBtTfEn4WGa+G3heeTxOBU6g+NLwsV7dbm6jjR5zSQcxg70k9WdHOex1JX11/BHl8PByeFGP+XuNv7Icrttzyha8qvwCcAOKq+pvprgq/PfljaC9HFoOr1xjnnZr7ktm/h1FLztnUDQfen5ZzzQ/bSqz1heNdqeUwwf1Of8rKK6Cn1T+/GLgXzPz5Mw8A/hd4Ob010Rn9f/+9n1ueyM2eswlHcRsiiNJ/TlQDm/bY/rtOua7rBz2uuLba/xqkL1lj+kDU97I+XXg+RFxFEUPNs+hCPrdrNbUb9hed18y80zgzM7xEbHanOkzfW7rO+Xw5uvNGBHjFE10/qBsknM4xb0Tq18myMyvRcR3gV/sY9urf32ZiIgbDLKdPRs/5pIOYl6xl6T+nF8OHxAR3S6KPLgcfhYgMy+j6Nnl9j1ucn1Al3FQ9PACRfOY7fQi4GrgFWXQ7eZoiqYxF/S5zk3tS0QcATyaIqyf0ediv14Oe/Wys7ruG1I0wdmbmfMdk2/S8XO/fzVZAr4I3IHuzwBo3/4hEXGjPtcLxc3D32M47fclNYzBXpL6UPbDfgYwRnHD5U9ExPHAFEVb8g+1Tfo7is/Z15YPS1qd/46d62iztxz+eo/pQ1E+4fVvKK4Qv6hzekTchKK5zvmZeWmfq91bDrvuS0Qc1mXczSgehnUE8IrMvLpt2jERcb0r8uUXp78qf3z3OjW9BLgbxZN2gZ98Cfsm8PDVL23lA6cOo7hJd03lXz5Oong41Zsj4qnt/99tdd4T+AR9Ntkpu1e9DcWXkL6f9ivp4GVTHEnq37MoenP584h4GEU3jqv92F9HcUNre5eEr6foHvK3gXtExCco2ur/FkX3mI/j+jeHfj4ivgg8ZAjNOtbzvym6vXxBRLwlM7/bNm2Cok/6D/S7sj725cSIeBHFF4ALKb5UPJqiWdObMvOUjvmfDLwoIj5J0Q/95RQ3KD+S4ur6PwN/0aueMli/HHhRlxuaX0/xVNizIuI/gKcAVwBv7XNflyLiCRTdcL4LeHlE7KX4q8MOiv7zj6fod7/f9vIPK4d9H3NJBzev2EtSnzLzqxQB7RSKJhJ/RNGDyseA+7d32VjOfyVFE523ULTNf0H58/8GXlvOdhnX93/K+R/WZdrQZOaF5bYP4/oPRDoRuIZ1HsDUxVr7ci6wD3g4xbF8IkVvOI/IzG5/0TgT+ChFmJ8CXkjRw8+nyvoelZnXdCui7Kv/7cB/0D2sv4Ui9N8R+ANgBXh4Zva6yfl6MvMjZW2voGg+8ySKtvxPoej95xXAXTewzhMpvhgY7CX1JfzrniRtv4h4JvA24FmZeWrHtMOBrwDnZOZm+6ofmIj4eWA/sJiZMxtcdqT2pS4i4lco+tJ/eWZ2e96BJF2PV+wlaYjK3mY6x92J4urwtcBHOqeXbb5fCTwmIo4bepHreynwY4qaN2QE96UuXk3RY9FfVl2IpPqwjb0kDdcHyl5QzqN40uoY8CjgZhRPpP1Wj+VOpbiBtFf3mtuivAn0QuBpZVOdzRiJfamL8gbi84E9ZXMuSeqLTXEkaYgi4g+BpwG/QHET5RUUoe2vMvODVdYmSWoWg70kSZLUALaxlyRJkhrAYC9JkiQ1gMFekiRJagCDvSRJktQABntJkiSpAQz2kiRJUgMY7CVJkqQGMNhLkiRJDWCwlyRJkhrAYC9JkiQ1gMFekiRJagCDvSRJktQABntJkiSpAf4fnit7ewcbxcoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 379
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.print_summary(3, model=\"stratified age and wexp\")\n",
    "cph.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportional hazard assumption looks okay.\n"
     ]
    }
   ],
   "source": [
    "cph.check_assumptions(rossi_strata_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Introduce time-varying covariates\n",
    "\n",
    "Our second option to correct variables that violate the proportional hazard assumption is to model the time-varying component directly. This is done in two steps. The first is to transform your dataset into _episodic format_. This means that we split a subject from a single row into $n$ new rows, and each new row represents some time period for the subject. It's okay that the variables are static over this new time periods - we'll introduce some time-varying covariates later.\n",
    "\n",
    "See below for how to do this in _lifelines_:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stop</th>\n",
       "      <th>start</th>\n",
       "      <th>arrest</th>\n",
       "      <th>age</th>\n",
       "      <th>fin</th>\n",
       "      <th>id</th>\n",
       "      <th>mar</th>\n",
       "      <th>paro</th>\n",
       "      <th>prio</th>\n",
       "      <th>race</th>\n",
       "      <th>wexp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    stop  start  arrest  age  fin  id  mar  paro  prio  race  wexp\n",
       "0    1.0    0.0       0   27    0   0    0     1     3     1     0\n",
       "1    2.0    1.0       0   27    0   0    0     1     3     1     0\n",
       "2    3.0    2.0       0   27    0   0    0     1     3     1     0\n",
       "3    4.0    3.0       0   27    0   0    0     1     3     1     0\n",
       "4    5.0    4.0       0   27    0   0    0     1     3     1     0\n",
       "5    6.0    5.0       0   27    0   0    0     1     3     1     0\n",
       "6    7.0    6.0       0   27    0   0    0     1     3     1     0\n",
       "7    8.0    7.0       0   27    0   0    0     1     3     1     0\n",
       "8    9.0    8.0       0   27    0   0    0     1     3     1     0\n",
       "9   10.0    9.0       0   27    0   0    0     1     3     1     0\n",
       "10  11.0   10.0       0   27    0   0    0     1     3     1     0\n",
       "11  12.0   11.0       0   27    0   0    0     1     3     1     0\n",
       "12  13.0   12.0       0   27    0   0    0     1     3     1     0\n",
       "13  14.0   13.0       0   27    0   0    0     1     3     1     0\n",
       "14  15.0   14.0       0   27    0   0    0     1     3     1     0\n",
       "15  16.0   15.0       0   27    0   0    0     1     3     1     0\n",
       "16  17.0   16.0       0   27    0   0    0     1     3     1     0\n",
       "17  18.0   17.0       0   27    0   0    0     1     3     1     0\n",
       "18  19.0   18.0       0   27    0   0    0     1     3     1     0\n",
       "19  20.0   19.0       1   27    0   0    0     1     3     1     0\n",
       "20   1.0    0.0       0   18    0   1    0     1     8     1     0\n",
       "21   2.0    1.0       0   18    0   1    0     1     8     1     0\n",
       "22   3.0    2.0       0   18    0   1    0     1     8     1     0\n",
       "23   4.0    3.0       0   18    0   1    0     1     8     1     0\n",
       "24   5.0    4.0       0   18    0   1    0     1     8     1     0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifelines.utils import to_episodic_format\n",
    "\n",
    "# the time_gaps parameter specifies how large or small you want the periods to be. \n",
    "rossi_long = to_episodic_format(rossi, duration_col='week', event_col='arrest', time_gaps=1.)\n",
    "rossi_long.head(25)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each subject is given a new id (but can be specified as well if already provided in the dataframe). This id is used to track subjects over time. Notice the `arrest` col is 0 for all periods prior to their (possible) event as well. \n",
    "\n",
    "Above I mentioned there were two steps to correct `age`. The first was to convert to a episodic format. The second is to create an interaction term between `age` and `stop`. This is a time-varying variable.\n",
    "\n",
    "Instead of `CoxPHFitter`, we must use `CoxTimeVaryingFitter` instead since we are working with a episodic dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "rossi_long['time*age'] = rossi_long['age'] * rossi_long['stop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxTimeVaryingFitter: fitted with 19809 periods, 432 subjects, 114 events>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifelines import CoxTimeVaryingFitter\n",
    "ctv = CoxTimeVaryingFitter()\n",
    "\n",
    "ctv.fit(rossi_long, \n",
    "        id_col='id', \n",
    "        event_col='arrest', \n",
    "        start_col='start', \n",
    "        stop_col='stop', \n",
    "        strata=['wexp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  \n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxTimeVaryingFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strata</th>\n",
       "      <td>[wexp]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of subjects</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of periods</th>\n",
       "      <td>19809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-575.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2019-11-17 14:00:20 UTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>age * time interaction</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.073</td>\n",
       "      <td>1.075</td>\n",
       "      <td>0.040</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.995</td>\n",
       "      <td>1.163</td>\n",
       "      <td>1.830</td>\n",
       "      <td>0.067</td>\n",
       "      <td>3.893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>-0.386</td>\n",
       "      <td>0.680</td>\n",
       "      <td>0.191</td>\n",
       "      <td>-0.760</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.989</td>\n",
       "      <td>-2.018</td>\n",
       "      <td>0.044</td>\n",
       "      <td>4.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>-0.397</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.382</td>\n",
       "      <td>-1.147</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.318</td>\n",
       "      <td>1.422</td>\n",
       "      <td>-1.039</td>\n",
       "      <td>0.299</td>\n",
       "      <td>1.743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>-0.098</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.196</td>\n",
       "      <td>-0.481</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.618</td>\n",
       "      <td>1.330</td>\n",
       "      <td>-0.501</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>0.090</td>\n",
       "      <td>1.094</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.146</td>\n",
       "      <td>1.035</td>\n",
       "      <td>1.158</td>\n",
       "      <td>3.152</td>\n",
       "      <td>0.002</td>\n",
       "      <td>9.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.295</td>\n",
       "      <td>1.343</td>\n",
       "      <td>0.308</td>\n",
       "      <td>-0.310</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.733</td>\n",
       "      <td>2.458</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.340</td>\n",
       "      <td>1.558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time*age</th>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.998</td>\n",
       "      <td>-3.337</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10.203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctv.print_summary(3, model=\"age * time interaction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1064d6e80>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 410
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctv.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above scaled Schoenfeld residual plots for `age`, we can see there is a slight negative effect for higher time values. This is confirmed in the output of the `CoxTimeVaryingFitter`: we see that the coefficient for `time*age` is -0.005.\n",
    "\n",
    "#### Conclusion\n",
    "\n",
    "The point estimates and the standard errors are very close to each other using either option, we can feel confident that either approach is okay to proceed. "
   ]
  }
 ],
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