{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c9f3ba8d-da52-4038-8f63-2b513fd51fe4",
   "metadata": {},
   "source": [
    "# MIMIC-II IAC Survival Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28605891-5073-4ef9-9cbc-1adbbe99d604",
   "metadata": {},
   "source": [
    "This tutorial explores the MIMIC-II IAC dataset. It was created for the purpose of a case study in the book: Secondary Analysis of Electronic Health Records, published by Springer in 2016. In particular, the dataset was used throughout Chapter 16 (Data Analysis) by Raffa J. et al. to investigate the effectiveness of indwelling arterial catheters in hemodynamically stable patients with respiratory failure for mortality outcomes. The dataset is derived from MIMIC-II, the publicly-accessible critical care database. It contains summary clinical data and  outcomes for 1,776 patients.\n",
    "\n",
    "Reference: \n",
    "\n",
    "[1] Critical Data, M.I.T., 2016. Secondary analysis of electronic health records (p. 427). Springer Nature. (https://link.springer.com/book/10.1007/978-3-319-43742-2)\n",
    "\n",
    "[2] https://github.com/MIT-LCP/critical-data-book/tree/master/part_ii/chapter_16/jupyter\n",
    "\n",
    "[3] https://stackoverflow.com/questions/27328623/anova-test-for-glm-in-python/60769343#60769343"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e77910ed-82d4-4b4c-93a6-7f3fae8b2cb1",
   "metadata": {},
   "source": [
    "More details on the dataset can be found here: https://physionet.org/content/mimic2-iaccd/1.0/."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd31bd3-c945-4314-be00-985be720d6e9",
   "metadata": {},
   "source": [
    "## Importing ehrapy and setting plotting parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ea172c3a-e8aa-469a-ba7c-5d4730fee1e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ehrapy as ep\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "from itertools import product\n",
    "from lifelines import CoxPHFitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d845678f-4473-48c1-9a9d-8dcc03ed41b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3f45a5f8-409f-4c0d-802d-616473ae2810",
   "metadata": {},
   "outputs": [],
   "source": [
    "ep.print_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d01b06d-cd7a-4caf-8a41-e9ee2202e897",
   "metadata": {},
   "source": [
    "## MIMIC-II dataset preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "95a30585-8194-4dc6-986d-e27c21a124c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "adata = ep.dt.mimic_2(encoded=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8c9f2c-817b-4366-8764-2ae64785b2dd",
   "metadata": {},
   "source": [
    "It is also possible to get the MIMIC-II dataset already pre-encoded by setting the encoded flag to 'True'. ehrapy's default encoding is a simple label encoding in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd9592a-ef02-47d7-8576-e73aac96b8ae",
   "metadata": {},
   "source": [
    "The MIMIC-II dataset has 1776 patients as described above with 46 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "29f0bda7-471c-4387-b259-7bb1535093a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 1776 × 46\n",
       "    layers: 'original'"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ffeab3-d1e6-4c91-a6c3-b6d641a9f4af",
   "metadata": {},
   "source": [
    "After one-hot encoding the two columns we've expanded our matrix from 46 to 54 features.\n",
    "Let's verify that we've indeed encoded all columns and are ready to proceed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9df205a5",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c54d865e",
   "metadata": {},
   "source": [
    "### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04daa32c",
   "metadata": {},
   "source": [
    "Linear regression provides the foundation for many types of analyses we perform on health data.  In the simplest scenario, we try to relate one continuous outcome, $y$, to a single continuous covariate, $x$, by trying to find values for $\\beta_0$ and $\\beta_1$ so that the following equation describes our observations:\n",
    "$y=\\beta_0 + \\beta_1 \\times x$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86ec200c",
   "metadata": {},
   "source": [
    "It is always a good idea to visualize the data when you can, which allows one to assess if the subsequent analysis corresponds to what you could see with your eyes.  In this case, a scatter plot has been used, producing the scattered points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ed85cc55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 728.72x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = ep.pl.scatter(adata, x=\"pco2_first\", y=\"tco2_first\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2966db28",
   "metadata": {},
   "source": [
    "Finding the best fit line for the scatter plot above using `ep.tl.ols (ehrapy.tools.ols)` is relatively straightforward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "1b0b2d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "var_names = [\"pco2_first\", \"tco2_first\", \"gender_num\"]\n",
    "formula = \"tco2_first ~ pco2_first\"\n",
    "co2_lm = ep.tl.ols(adata, var_names, formula, missing=\"drop\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8697fa9",
   "metadata": {},
   "source": [
    "Dissecting this command from left to right: The `co2_lm =` part assigns the right part of the command to a new variable called `co2_lm`.  The right side of this command runs the `OLS` function in `Python`. The basic `OLS` command has two parts.  The first is the formula which has the general syntax `outcome ~ covariates`.  Here, our outcome variable is called `tco2_first` and we are just fitting one covariate, `pco2_first`, so our formula is `tco2_first ~ pco2_first`.  The second argument is separated by a comma and is specifying the data frame to use.  In our case, the data frame is called `dat`, so we pass `data=dat`, noting that both `tco2_first` and `pco2_first` are columns in the dataframe `dat`. The overall procedure of specifying a model formula (`tco2_first ~ pco2_first`), a data frame (`data=dat`) and passing it an appropriate `Python` function (`OLS`) will be used throughout this chapter, and is the foundation for many types of statistical modeling in `Python`.\n",
    "\n",
    "We would like to see some information about the model we just fit, and often a good way of doing this is to run the `summary` command on the object we created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "0bcd26ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>tco2_first</td>    <th>  R-squared:         </th> <td>   0.265</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.264</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   571.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 05 Feb 2025</td> <th>  Prob (F-statistic):</th> <td>3.52e-108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:49:23</td>     <th>  Log-Likelihood:    </th> <td> -4609.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1590</td>      <th>  AIC:               </th> <td>   9222.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1588</td>      <th>  BIC:               </th> <td>   9233.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td>   16.2109</td> <td>    0.360</td> <td>   45.071</td> <td> 0.000</td> <td>   15.505</td> <td>   16.916</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pco2_first</th> <td>    0.1886</td> <td>    0.008</td> <td>   23.912</td> <td> 0.000</td> <td>    0.173</td> <td>    0.204</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>94.233</td> <th>  Durbin-Watson:     </th> <td>   1.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 195.722</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.389</td> <th>  Prob(JB):          </th> <td>3.16e-43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.532</td> <th>  Cond. No.          </th> <td>    149.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &   tco2\\_first    & \\textbf{  R-squared:         } &     0.265   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.264   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     571.8   \\\\\n",
       "\\textbf{Date:}             & Wed, 05 Feb 2025 & \\textbf{  Prob (F-statistic):} & 3.52e-108   \\\\\n",
       "\\textbf{Time:}             &     09:49:23     & \\textbf{  Log-Likelihood:    } &   -4609.1   \\\\\n",
       "\\textbf{No. Observations:} &        1590      & \\textbf{  AIC:               } &     9222.   \\\\\n",
       "\\textbf{Df Residuals:}     &        1588      & \\textbf{  BIC:               } &     9233.   \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                     & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}   &      16.2109  &        0.360     &    45.071  &         0.000        &       15.505    &       16.916     \\\\\n",
       "\\textbf{pco2\\_first} &       0.1886  &        0.008     &    23.912  &         0.000        &        0.173    &        0.204     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 94.233 & \\textbf{  Durbin-Watson:     } &    1.963  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &  195.722  \\\\\n",
       "\\textbf{Skew:}          & -0.389 & \\textbf{  Prob(JB):          } & 3.16e-43  \\\\\n",
       "\\textbf{Kurtosis:}      &  4.532 & \\textbf{  Cond. No.          } &     149.  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             tco2_first   R-squared:                       0.265\n",
       "Model:                            OLS   Adj. R-squared:                  0.264\n",
       "Method:                 Least Squares   F-statistic:                     571.8\n",
       "Date:                Wed, 05 Feb 2025   Prob (F-statistic):          3.52e-108\n",
       "Time:                        09:49:23   Log-Likelihood:                -4609.1\n",
       "No. Observations:                1590   AIC:                             9222.\n",
       "Df Residuals:                    1588   BIC:                             9233.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     16.2109      0.360     45.071      0.000      15.505      16.916\n",
       "pco2_first     0.1886      0.008     23.912      0.000       0.173       0.204\n",
       "==============================================================================\n",
       "Omnibus:                       94.233   Durbin-Watson:                   1.963\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              195.722\n",
       "Skew:                          -0.389   Prob(JB):                     3.16e-43\n",
       "Kurtosis:                       4.532   Cond. No.                         149.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_lm_result = co2_lm.fit()\n",
    "co2_lm_result.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f724fd7c",
   "metadata": {},
   "source": [
    "This outputs information about the `OLS` object we created in the previous step.  The first part recalls the model we fit, which is useful when we have fit many models, and are trying to compare them.  The second part lists some summary information about what are called residuals -- a topic we will cover later on in this book (Chapter 2.6).  Next lists the coefficient estimates -- these are the $\\hat{\\beta}_0$, `(Intercept)`, and $\\hat{\\beta}_1$, `pco2_first`, parameters in the best fit line we are trying to estimate.  This output is telling us that the best fit equation for the data is:\n",
    "\n",
    "`tco2_first` = 16.21 + 0.189 $\\times$ `pco2_first`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04927b2d",
   "metadata": {},
   "source": [
    "### Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed7bbaa",
   "metadata": {},
   "source": [
    "Model selection is the procedure to select the best model from a list (perhaps rather large list) of candidate models. Different approaches and techniques exist for this procedure.  We will cover some basics here, as more complicated techniques will be covered in a later chapter.  In the simplest case, we have two models, and we want to know which one we should use.\n",
    "\n",
    "We will begin by examining if the relationship between TCO2 and PCO2 is more complicated than the model we fit in the previous section.  If you recall, we fit a model where we considered a linear `pco2_first` term: `tco2_first` = $\\beta_0 + \\beta_1 \\times$ `pco2_first`.  One may wonder if including a quadratic term would fit the data better, i.e. whether:\n",
    "\n",
    "`tco2_first` = $\\beta_0 + \\beta_1 \\times$ `pco2_first` + $\\beta_2 \\times$ `pco2_first`$^2$,\n",
    "\n",
    "is a better model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "408a42c0",
   "metadata": {},
   "source": [
    "Adding a quadratic term (or any other function) is quite easy using the `OLS` function. Fitting this model, and running the `summary` function for the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "ea0bf4f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>tco2_first</td>    <th>  R-squared:         </th> <td>   0.265</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.264</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   285.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 05 Feb 2025</td> <th>  Prob (F-statistic):</th> <td>1.04e-106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:49:23</td>     <th>  Log-Likelihood:    </th> <td> -4609.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1590</td>      <th>  AIC:               </th> <td>   9224.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1587</td>      <th>  BIC:               </th> <td>   9240.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>             <td>   16.0916</td> <td>    0.771</td> <td>   20.862</td> <td> 0.000</td> <td>   14.579</td> <td>   17.605</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pco2_first</th>            <td>    0.1930</td> <td>    0.027</td> <td>    7.231</td> <td> 0.000</td> <td>    0.141</td> <td>    0.245</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.square(pco2_first)</th> <td>-3.569e-05</td> <td>    0.000</td> <td>   -0.175</td> <td> 0.861</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>93.522</td> <th>  Durbin-Watson:     </th> <td>   1.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 194.937</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.385</td> <th>  Prob(JB):          </th> <td>4.68e-43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.533</td> <th>  Cond. No.          </th> <td>1.94e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.94e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}         &   tco2\\_first    & \\textbf{  R-squared:         } &     0.265   \\\\\n",
       "\\textbf{Model:}                 &       OLS        & \\textbf{  Adj. R-squared:    } &     0.264   \\\\\n",
       "\\textbf{Method:}                &  Least Squares   & \\textbf{  F-statistic:       } &     285.7   \\\\\n",
       "\\textbf{Date:}                  & Wed, 05 Feb 2025 & \\textbf{  Prob (F-statistic):} & 1.04e-106   \\\\\n",
       "\\textbf{Time:}                  &     09:49:23     & \\textbf{  Log-Likelihood:    } &   -4609.1   \\\\\n",
       "\\textbf{No. Observations:}      &        1590      & \\textbf{  AIC:               } &     9224.   \\\\\n",
       "\\textbf{Df Residuals:}          &        1587      & \\textbf{  BIC:               } &     9240.   \\\\\n",
       "\\textbf{Df Model:}              &           2      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}       &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}              &      16.0916  &        0.771     &    20.862  &         0.000        &       14.579    &       17.605     \\\\\n",
       "\\textbf{pco2\\_first}            &       0.1930  &        0.027     &     7.231  &         0.000        &        0.141    &        0.245     \\\\\n",
       "\\textbf{np.square(pco2\\_first)} &   -3.569e-05  &        0.000     &    -0.175  &         0.861        &       -0.000    &        0.000     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 93.522 & \\textbf{  Durbin-Watson:     } &    1.963  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &  194.937  \\\\\n",
       "\\textbf{Skew:}          & -0.385 & \\textbf{  Prob(JB):          } & 4.68e-43  \\\\\n",
       "\\textbf{Kurtosis:}      &  4.533 & \\textbf{  Cond. No.          } & 1.94e+04  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 1.94e+04. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             tco2_first   R-squared:                       0.265\n",
       "Model:                            OLS   Adj. R-squared:                  0.264\n",
       "Method:                 Least Squares   F-statistic:                     285.7\n",
       "Date:                Wed, 05 Feb 2025   Prob (F-statistic):          1.04e-106\n",
       "Time:                        09:49:23   Log-Likelihood:                -4609.1\n",
       "No. Observations:                1590   AIC:                             9224.\n",
       "Df Residuals:                    1587   BIC:                             9240.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "Intercept                16.0916      0.771     20.862      0.000      14.579      17.605\n",
       "pco2_first                0.1930      0.027      7.231      0.000       0.141       0.245\n",
       "np.square(pco2_first) -3.569e-05      0.000     -0.175      0.861      -0.000       0.000\n",
       "==============================================================================\n",
       "Omnibus:                       93.522   Durbin-Watson:                   1.963\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              194.937\n",
       "Skew:                          -0.385   Prob(JB):                     4.68e-43\n",
       "Kurtosis:                       4.533   Cond. No.                     1.94e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.94e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = \"tco2_first ~ pco2_first + np.square(pco2_first)\"\n",
    "\n",
    "o2_quad_lm = ep.tl.ols(adata, var_names, formula, missing=\"drop\")\n",
    "co2_quad_lm_result = o2_quad_lm.fit()\n",
    "co2_quad_lm_result.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a6bec8",
   "metadata": {},
   "source": [
    "Looking first at the coefficients estimates, we see the best fit line is estimated as:\n",
    "\n",
    "`tco2_first` = 16.09 + 0.19 $\\times$ `pco2_first` + 0.00004 $\\times$ `pco2_first`$^2$.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13aab529",
   "metadata": {},
   "source": [
    "We can add both best fit lines to the former scattered points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "baf67f71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ep.pl.ols(\n",
    "    adata,\n",
    "    x=\"pco2_first\",\n",
    "    y=\"tco2_first\",\n",
    "    ols_results=[co2_lm_result, co2_quad_lm_result],\n",
    "    ols_color=[\"red\", \"blue\"],\n",
    "    xlabel=\"PCO2\",\n",
    "    ylabel=\"TCO2\",\n",
    "    show=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "443ce7eb",
   "metadata": {},
   "source": [
    "Figure Scatterplot of PCO2 (x-axis) and TCO2 (y-axis) along with linear regression estimates from the quadratic model (co2_quad_lm) and linear only model (co2_lm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c15fac",
   "metadata": {},
   "source": [
    "And one can see that the red (linear term only) and blue (linear and quadratic terms) fits are nearly identical.  This corresponds with the relatively small coefficient estimate for the `(pco2_first^2)` term.  The p-value for this coefficient is about 0.86, and at the 0.05 significance level we would likely conclude that a quadratic term is not necessary in our model to fit the data, as the linear term only model fits the data nearly as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d83a8b2",
   "metadata": {},
   "source": [
    "#### Statistical Interactions and Testing Nested Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a7024e1",
   "metadata": {},
   "source": [
    "We can also consider additional features in our model. For this, we can start with e.g. gender as a covariate, but no interaction.  We can do this by simply adding the variable `gender_num` to the previous formula for our `co2_lm` model fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d867ac3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>tco2_first</td>    <th>  R-squared:         </th> <td>   0.265</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.264</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   286.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 05 Feb 2025</td> <th>  Prob (F-statistic):</th> <td>7.60e-107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:49:23</td>     <th>  Log-Likelihood:    </th> <td> -4608.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1590</td>      <th>  AIC:               </th> <td>   9223.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1587</td>      <th>  BIC:               </th> <td>   9240.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td>   16.3044</td> <td>    0.378</td> <td>   43.166</td> <td> 0.000</td> <td>   15.564</td> <td>   17.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pco2_first</th> <td>    0.1889</td> <td>    0.008</td> <td>   23.922</td> <td> 0.000</td> <td>    0.173</td> <td>    0.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender_num</th> <td>   -0.1817</td> <td>    0.224</td> <td>   -0.812</td> <td> 0.417</td> <td>   -0.621</td> <td>    0.257</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>94.658</td> <th>  Durbin-Watson:     </th> <td>   1.964</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 194.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.394</td> <th>  Prob(JB):          </th> <td>4.57e-43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.524</td> <th>  Cond. No.          </th> <td>    160.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &   tco2\\_first    & \\textbf{  R-squared:         } &     0.265   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.264   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     286.1   \\\\\n",
       "\\textbf{Date:}             & Wed, 05 Feb 2025 & \\textbf{  Prob (F-statistic):} & 7.60e-107   \\\\\n",
       "\\textbf{Time:}             &     09:49:23     & \\textbf{  Log-Likelihood:    } &   -4608.7   \\\\\n",
       "\\textbf{No. Observations:} &        1590      & \\textbf{  AIC:               } &     9223.   \\\\\n",
       "\\textbf{Df Residuals:}     &        1587      & \\textbf{  BIC:               } &     9240.   \\\\\n",
       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                     & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}   &      16.3044  &        0.378     &    43.166  &         0.000        &       15.564    &       17.045     \\\\\n",
       "\\textbf{pco2\\_first} &       0.1889  &        0.008     &    23.922  &         0.000        &        0.173    &        0.204     \\\\\n",
       "\\textbf{gender\\_num} &      -0.1817  &        0.224     &    -0.812  &         0.417        &       -0.621    &        0.257     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 94.658 & \\textbf{  Durbin-Watson:     } &    1.964  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &  194.982  \\\\\n",
       "\\textbf{Skew:}          & -0.394 & \\textbf{  Prob(JB):          } & 4.57e-43  \\\\\n",
       "\\textbf{Kurtosis:}      &  4.524 & \\textbf{  Cond. No.          } &     160.  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             tco2_first   R-squared:                       0.265\n",
       "Model:                            OLS   Adj. R-squared:                  0.264\n",
       "Method:                 Least Squares   F-statistic:                     286.1\n",
       "Date:                Wed, 05 Feb 2025   Prob (F-statistic):          7.60e-107\n",
       "Time:                        09:49:23   Log-Likelihood:                -4608.7\n",
       "No. Observations:                1590   AIC:                             9223.\n",
       "Df Residuals:                    1587   BIC:                             9240.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     16.3044      0.378     43.166      0.000      15.564      17.045\n",
       "pco2_first     0.1889      0.008     23.922      0.000       0.173       0.204\n",
       "gender_num    -0.1817      0.224     -0.812      0.417      -0.621       0.257\n",
       "==============================================================================\n",
       "Omnibus:                       94.658   Durbin-Watson:                   1.964\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              194.982\n",
       "Skew:                          -0.394   Prob(JB):                     4.57e-43\n",
       "Kurtosis:                       4.524   Cond. No.                         160.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = \"tco2_first ~ pco2_first + gender_num\"\n",
    "var_names = [\"tco2_first\", \"pco2_first\", \"gender_num\"]\n",
    "co2_gender_lm = ep.tl.ols(adata, var_names, formula, missing=\"drop\")\n",
    "co2_gender_lm_result = co2_gender_lm.fit()\n",
    "co2_gender_lm_result.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eeb520f",
   "metadata": {},
   "source": [
    "This output is very similar to what we had before, but now there's a `gender_num` term as well.  The `1` is present in the first column after `gender_num`, and it tells us who this coefficient is relevant to (subjects with `1` for the `gender_num` -- men).  This is always relative to the baseline group, and in this case this is women.  The estimate is negative, meaning that the line fit for males will be below the line for females.  Plotting this fit curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "0bad1e7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# s stands for slope of the line, i stands for intercept of the line\n",
    "s_female = co2_gender_lm_result.params[1]\n",
    "i_female = co2_gender_lm_result.params[0]\n",
    "s_male = co2_gender_lm_result.params[1]\n",
    "i_male = co2_gender_lm_result.params[0] + co2_gender_lm_result.params[2]\n",
    "ep.pl.ols(\n",
    "    lines=[(s_female, i_female), (s_male, i_male)],\n",
    "    lines_color=[\"k\", \"r\"],\n",
    "    lines_label=[\"Female\", \"Male\"],\n",
    "    xlabel=\"PCO2\",\n",
    "    ylabel=\"TCO2\",\n",
    "    xlim=(0, 40),\n",
    "    ylim=(15, 25),\n",
    "    show=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "767dd36e",
   "metadata": {},
   "source": [
    "We see that the lines are parallel, but almost indistinguishable.  In fact, this plot has been cropped in order to see any difference at all.  From the estimate from the `summary` output above, the difference between the two lines is -0.182 mmol/L, which is quite small, so perhaps this isn't too surprising.  We can also see in the above `summary` output that the p-value is about 0.42, and we would likely *not* reject the null hypothesis that the true value of the `gender_num` coefficient is zero."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b6181f",
   "metadata": {},
   "source": [
    "Next, we can explore our model with an interaction between `pco2_first` and `gender_num`.  To add an interaction between two variables use the `*` operator within a model formula.  By default, `Python` will add all of the main effects (variables contained in the interaction) to the model as well, so simply adding `pco2_first*gender_num` will add effects for `pco2_first` and `gender_num` in addition to the interaction between them to the model fit.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "e498860e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>tco2_first</td>    <th>  R-squared:         </th> <td>   0.266</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.265</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   191.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 05 Feb 2025</td> <th>  Prob (F-statistic):</th> <td>5.09e-106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:49:23</td>     <th>  Log-Likelihood:    </th> <td> -4607.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1590</td>      <th>  AIC:               </th> <td>   9223.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1586</td>      <th>  BIC:               </th> <td>   9245.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>             <td>   15.8544</td> <td>    0.489</td> <td>   32.443</td> <td> 0.000</td> <td>   14.896</td> <td>   16.813</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pco2_first</th>            <td>    0.1994</td> <td>    0.011</td> <td>   18.585</td> <td> 0.000</td> <td>    0.178</td> <td>    0.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender_num</th>            <td>    0.8144</td> <td>    0.722</td> <td>    1.128</td> <td> 0.260</td> <td>   -0.602</td> <td>    2.231</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pco2_first:gender_num</th> <td>   -0.0230</td> <td>    0.016</td> <td>   -1.450</td> <td> 0.147</td> <td>   -0.054</td> <td>    0.008</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>97.108</td> <th>  Durbin-Watson:     </th> <td>   1.964</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 200.324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.403</td> <th>  Prob(JB):          </th> <td>3.16e-44</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.541</td> <th>  Cond. No.          </th> <td>    399.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &   tco2\\_first    & \\textbf{  R-squared:         } &     0.266   \\\\\n",
       "\\textbf{Model:}                  &       OLS        & \\textbf{  Adj. R-squared:    } &     0.265   \\\\\n",
       "\\textbf{Method:}                 &  Least Squares   & \\textbf{  F-statistic:       } &     191.6   \\\\\n",
       "\\textbf{Date:}                   & Wed, 05 Feb 2025 & \\textbf{  Prob (F-statistic):} & 5.09e-106   \\\\\n",
       "\\textbf{Time:}                   &     09:49:23     & \\textbf{  Log-Likelihood:    } &   -4607.7   \\\\\n",
       "\\textbf{No. Observations:}       &        1590      & \\textbf{  AIC:               } &     9223.   \\\\\n",
       "\\textbf{Df Residuals:}           &        1586      & \\textbf{  BIC:               } &     9245.   \\\\\n",
       "\\textbf{Df Model:}               &           3      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                 & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}               &      15.8544  &        0.489     &    32.443  &         0.000        &       14.896    &       16.813     \\\\\n",
       "\\textbf{pco2\\_first}             &       0.1994  &        0.011     &    18.585  &         0.000        &        0.178    &        0.220     \\\\\n",
       "\\textbf{gender\\_num}             &       0.8144  &        0.722     &     1.128  &         0.260        &       -0.602    &        2.231     \\\\\n",
       "\\textbf{pco2\\_first:gender\\_num} &      -0.0230  &        0.016     &    -1.450  &         0.147        &       -0.054    &        0.008     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 97.108 & \\textbf{  Durbin-Watson:     } &    1.964  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &  200.324  \\\\\n",
       "\\textbf{Skew:}          & -0.403 & \\textbf{  Prob(JB):          } & 3.16e-44  \\\\\n",
       "\\textbf{Kurtosis:}      &  4.541 & \\textbf{  Cond. No.          } &     399.  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             tco2_first   R-squared:                       0.266\n",
       "Model:                            OLS   Adj. R-squared:                  0.265\n",
       "Method:                 Least Squares   F-statistic:                     191.6\n",
       "Date:                Wed, 05 Feb 2025   Prob (F-statistic):          5.09e-106\n",
       "Time:                        09:49:23   Log-Likelihood:                -4607.7\n",
       "No. Observations:                1590   AIC:                             9223.\n",
       "Df Residuals:                    1586   BIC:                             9245.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "Intercept                15.8544      0.489     32.443      0.000      14.896      16.813\n",
       "pco2_first                0.1994      0.011     18.585      0.000       0.178       0.220\n",
       "gender_num                0.8144      0.722      1.128      0.260      -0.602       2.231\n",
       "pco2_first:gender_num    -0.0230      0.016     -1.450      0.147      -0.054       0.008\n",
       "==============================================================================\n",
       "Omnibus:                       97.108   Durbin-Watson:                   1.964\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              200.324\n",
       "Skew:                          -0.403   Prob(JB):                     3.16e-44\n",
       "Kurtosis:                       4.541   Cond. No.                         399.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = \"tco2_first ~ pco2_first * gender_num\"\n",
    "var_names = [\"tco2_first\", \"pco2_first\", \"gender_num\"]\n",
    "\n",
    "co2_gender_interaction_lm = ep.tl.ols(adata, var_names, formula, missing=\"drop\")\n",
    "co2_gender_interaction_lm_result = co2_gender_interaction_lm.fit()\n",
    "co2_gender_interaction_lm_result.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b0ee86",
   "metadata": {},
   "source": [
    "The estimated coefficients are $\\hat{\\beta}_0, \\hat{\\beta}_1, \\hat{\\beta}_2$ and $\\hat{\\beta}_3$, respectively, and we can determine the best fit lines for men:\n",
    "\n",
    "\n",
    "`tco2_first` = $(15.85 + 0.81)$ + $(0.20 - 0.023)$ $\\times$ `pco2_first` =  $16.67$ + $0.18$ $\\times$ `pco2_first`,\n",
    "\n",
    "and for women:\n",
    "\n",
    "`tco2_first` = $15.85$ + $0.20$ $\\times$ `pco2_first`.\n",
    "\n",
    "Based on this, the men's intercept should be higher, but their slope should be not as steep, relative to the women.  Let's check this and add the new model fits as dotted lines and add a legend to the above figure.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "10e22ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# s stands for slope of the line, i stands for intercept of the line\n",
    "s_female_interaction = co2_gender_interaction_lm_result.params[1]\n",
    "i_female_interaction = co2_gender_interaction_lm_result.params[0]\n",
    "s_male_interaction = (\n",
    "    co2_gender_interaction_lm_result.params[1]\n",
    "    + co2_gender_interaction_lm_result.params[3]\n",
    ")\n",
    "i_male_interaction = (\n",
    "    co2_gender_interaction_lm_result.params[0]\n",
    "    + co2_gender_interaction_lm_result.params[2]\n",
    ")\n",
    "\n",
    "lines = [\n",
    "    (s_female, i_female),\n",
    "    (s_male, i_male),\n",
    "    (s_female_interaction, i_female_interaction),\n",
    "    (s_male_interaction, i_male_interaction),\n",
    "]\n",
    "lines_color = [\"k\", \"r\", \"k\", \"r\"]\n",
    "lines_style = [\"-\", \"-\", \":\", \":\"]\n",
    "lines_label = [\n",
    "    \"Female\",\n",
    "    \"Male\",\n",
    "    \"Female (Interaction Model)\",\n",
    "    \"Male (Interaction Model)\",\n",
    "]\n",
    "ep.pl.ols(\n",
    "    lines=lines,\n",
    "    lines_color=lines_color,\n",
    "    lines_style=lines_style,\n",
    "    lines_label=lines_label,\n",
    "    xlabel=\"PCO2\",\n",
    "    ylabel=\"TCO2\",\n",
    "    xlim=(0, 40),\n",
    "    ylim=(15, 25),\n",
    "    show=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6314674",
   "metadata": {},
   "source": [
    "Regression fits of PCO2 on TCO2 with gender (black female; red male; solid no interaction; dotted with interaction). Note both axes are cropped for illustration purposes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "381b9481",
   "metadata": {},
   "source": [
    "We can see that the fits generated from this plot are a little different than the one generated for a model without the interaction.  The biggest difference is that the dotted lines are no longer parallel.  This has some serious implications, particularly when it comes to interpreting our result.  First note that the estimated coefficient for the `gender_num` variable is now positive.  This means that at `pco2_first=0`, men (red) have higher `tco2_first` levels than women (black).  If you recall in the previous model fit, women had higher levels of `tco2_first` at all levels of `pco2_first`.  At some point around `pco2_first=35` this changes and women (black) have higher `tco2_first` levels than men (red).  This means that the effect of `gender_num` *may* vary as you change the level of `pco2_first`, and is why interactions are often referred to as effect modification in the epidemiological literature.  The effect need not change signs (i.e., the lines do not need to cross) over the observed range of values for an interaction to be present.\n",
    "\n",
    "The question remains, is the variable `gender_num` important?  We looked at this briefly when we examined the `t value` column in the no interaction model which included `gender_num`.  What if we wanted to test (simultaneously) the null hypothesis: $\\beta_2$  *and*  $\\beta_3=0$  There is a useful test known as the F-test which can help us in this exact scenario where we want to look at if we should use a larger model (more covariates) or use a smaller model (fewer covariates).  The F-test applies only to *nested models* -- the larger model *must* contain each covariate that is used in the smaller model, and the smaller model *cannot* contain covariates which are not in the larger model.  The interaction model and the model with gender are nested models since all the covariates in the model with gender are also in the larger interaction model.  An example of a non-nested model would be the quadratic model and the interaction model: the smaller (quadratic) model has a term (`pco2_first`$^2$) which is not in the larger (interaction) model.  An F-test would not be appropriate for this latter case.\n",
    "\n",
    "To perform an F-test, first fit the two models you wish to consider, and then run the `anova_lm` command passing the two model objects.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "4ab389ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df_resid</th>\n",
       "      <th>ssr</th>\n",
       "      <th>df_diff</th>\n",
       "      <th>ss_diff</th>\n",
       "      <th>F</th>\n",
       "      <th>Pr(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1588.0</td>\n",
       "      <td>30674.431382</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1586.0</td>\n",
       "      <td>30621.082804</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53.348578</td>\n",
       "      <td>1.381578</td>\n",
       "      <td>0.251484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   df_resid           ssr  df_diff    ss_diff         F    Pr(>F)\n",
       "0    1588.0  30674.431382      0.0        NaN       NaN       NaN\n",
       "1    1586.0  30621.082804      2.0  53.348578  1.381578  0.251484"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_lm(co2_lm_result, co2_gender_interaction_lm_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc66237",
   "metadata": {},
   "source": [
    "As you can see, the `anova_lm` command first lists the models it is considering.  Much of the rest of the information is beyond the scope of this chapter, but we will highlight the reported F-test p-value (`Pr(>F)`), which in this case is 0.2515.  In nested models, the null hypothesis is that all coefficients in the larger model and not in the smaller model are zero.  In the case we are testing, our null hypothesis is $\\beta_2$  *and*  $\\beta_3=0$.  Since the p-value exceeds the typically used significance level ($\\alpha=0.05$), we would not reject the null hypothesis, and likely say the smaller model explains the data just as well as the larger model.  If these were the only models we were considering, we would use the smaller model as our final model and report the final model in our results.  We will now discuss what exactly you should report and how you can interpret the results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1280d993",
   "metadata": {},
   "source": [
    "### Reporting and Interpreting Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a3e3d5",
   "metadata": {},
   "source": [
    "Confidence and Prediction Intervals\n",
    "\n",
    "As mentioned above, one method to quantify the uncertainty around coefficient estimates is by reporting the standard error.  Another commonly used method is to report a confidence interval, most commonly a 95\\% confidence interval.  A 95\\% confidence interval for $\\beta$ is an interval for which if the data were collected repeatedly, about 95\\% of the *intervals* would contain the *true value* of the parameter, $\\beta$, assuming the modeling assumptions are correct.\n",
    "\n",
    "To get 95\\% confidence intervals of coefficients, `Python` has a `confint` function, which you pass an `lm` object to.  It will then output 2.5\\% and 97.5\\% confidence interval limits for each coefficient.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "4078d57f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>15.505369</td>\n",
       "      <td>16.916349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pco2_first</th>\n",
       "      <td>0.173103</td>\n",
       "      <td>0.204040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0          1\n",
       "Intercept   15.505369  16.916349\n",
       "pco2_first   0.173103   0.204040"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_lm_result.conf_int()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c138abc",
   "metadata": {},
   "source": [
    "The 95\\% confidence interval for `pco2_first` is about 0.17-0.20, which may be slightly more informative than reporting the standard error.  Often people will look at if the confidence interval includes zero (no effect). Since it does not, and in fact since the interval is quite narrow and not very close to zero, this provides some additional evidence of its importance.  There is a well known link between hypothesis testing and confidence intervals which we will not get into detail here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7d5656",
   "metadata": {},
   "source": [
    "When plotting the data with the model fit, similar to Figure 16.2, it is a good idea to include some sort of assessment of uncertainty as well.  To do this in `Python`, we will first create a data frame with PCO2 levels which we would like to predict.  In this case, we would like to predict the outcome (TCO2) over the range of observed covariate (PCO2) values.  We do this by creating a data frame, where the variable names in the data frame must match the covariates used in the model.  In our case, we have only one covariate (`pco2_first`), and we predict the outcome over the range of covariate values we observed determined by the `min` and `max` functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "2d4a1566",
   "metadata": {},
   "outputs": [],
   "source": [
    "pco2_first = pd.DataFrame(adata[:, \"pco2_first\"].X).dropna().astype(int)\n",
    "data = {\"pco2_first\": [i for i in range(pco2_first.min()[0], pco2_first.max()[0])]}\n",
    "grid_pred = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "2f507975",
   "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>mean</th>\n",
       "      <th>obs_ci_lower</th>\n",
       "      <th>obs_ci_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.719434</td>\n",
       "      <td>9.078647</td>\n",
       "      <td>26.360220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.908005</td>\n",
       "      <td>9.268186</td>\n",
       "      <td>26.547825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean  obs_ci_lower  obs_ci_upper\n",
       "0  17.719434      9.078647     26.360220\n",
       "1  17.908005      9.268186     26.547825"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = co2_lm_result.get_prediction(grid_pred).summary_frame()[\n",
    "    [\"mean\", \"obs_ci_lower\", \"obs_ci_upper\"]\n",
    "]\n",
    "preds[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128de6d2",
   "metadata": {},
   "source": [
    "We have printed out the first two rows of our predictions, preds, which are the model’s predictions for PCO2 at 8 and 9. We can see that our predictions (mean) are about 0.18 apart, which make sense given our estimate of the slope (0.18). We also see that our 95 % prediction intervals are very wide, spanning about 9 (obs_ci_lower) to 26 (obs_ci_upper). This indicates that, despite coming up with a model which is very statisti- cally significant, we still have a lot of uncertainty about the predictions generated from such a model. It is a good idea to capture this quality when plotting how well your model fits by adding the interval lines as dotted lines. Let’s plot our final model fit, co2.lm, along with the scatterplot and prediction interval in the figure below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1f9ae924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ep.pl.ols(\n",
    "    adata,\n",
    "    x=\"pco2_first\",\n",
    "    y=\"tco2_first\",\n",
    "    ols_results=[co2_lm_result],\n",
    "    ols_color=[\"red\"],\n",
    "    lines=[\n",
    "        (grid_pred[\"pco2_first\"], preds[\"obs_ci_upper\"]),\n",
    "        (grid_pred[\"pco2_first\"], preds[\"obs_ci_lower\"]),\n",
    "    ],\n",
    "    lines_color=[\"k\", \"k\"],\n",
    "    lines_style=[\"--\", \"--\"],\n",
    "    xlabel=\"PCO2\",\n",
    "    ylabel=\"TCO2\",\n",
    "    show=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf2cb4",
   "metadata": {},
   "source": [
    "Scatterplot of PCO2 (x-axis) and TCO2 (y-axis) along with linear regression estimates from the linear only model (co2_lm). The dotted line represents 95 % prediction intervals for the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19f2299a",
   "metadata": {},
   "source": [
    "## Logistic Regression\n",
    "So far, we have considered how a _continuous_ variable can be modeled using our features. Another very common modelling task is the one of classification. Here, we are interested to model a _discrete_ label using our features. A very common scenario is to have a binary class label, for example whether a patient survived a 28-day observation window or not."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb180b89",
   "metadata": {},
   "source": [
    "### 2 * 2 Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "50d436a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "var_names = [\"age\", \"day_28_flg\", \"service_unit\"]\n",
    "dat = pd.DataFrame(adata[:, var_names].X, columns=var_names)\n",
    "dat[\"age\"] = dat[\"age\"].astype(\"float\").dropna(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abcc4ce5",
   "metadata": {},
   "source": [
    "Contingency tables are the best way to start to think about binary data.  A contingency table cross-tabulates the outcome across two or more levels of a covariate.  Let's begin by creating a new variable (`age.cat`) which dichotomizes `age` into two age categories: $\\le55$ and $>55$.  Note, because we are making age a discrete variable, we also change the data type to a factor.  This is similar to what we did for the `gender_num` variable when discussing linear regression in the previous subchapter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "e3d29bbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_cat\n",
       "<=55    923\n",
       ">55     853\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat[\"age_cat\"] = np.where(dat[\"age\"] <= 55, \"<=55\", \">55\")\n",
    "dat[\"age_cat\"] = dat[\"age_cat\"].astype(\"category\")\n",
    "dat[\"age_cat\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9165bb3",
   "metadata": {},
   "source": [
    "We would like to see how 28 day mortality is distributed among the age categories.  We can do so by constructing a contingency table, or in this case what is commonly referred to as a 2x2 table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "71ec4e6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>&lt;=55</th>\n",
       "      <td>883</td>\n",
       "      <td>40</td>\n",
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       "      <th>&gt;55</th>\n",
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       "        0    1\n",
       "<=55  883   40\n",
       ">55   610  243"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_cat_day_28_flg = pd.crosstab(index=dat[\"age_cat\"], columns=dat[\"day_28_flg\"])\n",
    "age_cat_day_28_flg.columns = [\"0\", \"1\"]\n",
    "age_cat_day_28_flg.index = [\"<=55\", \">55\"]\n",
    "age_cat_day_28_flg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f97cccf5",
   "metadata": {},
   "source": [
    "Now let us look at a slightly different case -- when the covariate takes on more than two values.  Such a variable is the `service_unit`.  Let's see how the deaths are distributed among the different units:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "b5c9a6b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FICU</th>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICU</th>\n",
       "      <td>605</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SICU</th>\n",
       "      <td>829</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0    1\n",
       "FICU   59    3\n",
       "MICU  605  127\n",
       "SICU  829  153"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deathbyservice = pd.crosstab(index=dat[\"service_unit\"], columns=dat[\"day_28_flg\"])\n",
    "deathbyservice.columns = [\"0\", \"1\"]\n",
    "deathbyservice.index = [\"FICU\", \"MICU\", \"SICU\"]\n",
    "deathbyservice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1f1c652",
   "metadata": {},
   "source": [
    "we can get frequencies of these service units by applying the `prop.table` function to our cross-tabulated table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c43a435a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FICU</th>\n",
       "      <td>0.951613</td>\n",
       "      <td>0.048387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICU</th>\n",
       "      <td>0.826503</td>\n",
       "      <td>0.173497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SICU</th>\n",
       "      <td>0.844196</td>\n",
       "      <td>0.155804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1\n",
       "FICU  0.951613  0.048387\n",
       "MICU  0.826503  0.173497\n",
       "SICU  0.844196  0.155804"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deathbyservice.div(deathbyservice[\"0\"] + deathbyservice[\"1\"], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ba200f",
   "metadata": {},
   "source": [
    "It appears as though the `FICU` may have a lower rate of death than either the `MICU` or `SICU`.  To compute an odds ratios, first compute the odds:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "662a6bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FICU    0.050847\n",
       "MICU    0.209917\n",
       "SICU    0.184560\n",
       "dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deathbyservice[\"1\"].div(deathbyservice[\"0\"], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df251fad",
   "metadata": {},
   "source": [
    "And then we need to pick which of `FICU`, `MICU` or `SICU` will serve as the reference or baseline group.  This is the group which the other two groups will be compared to.  Again the choice is arbitrary, but should be dictated by the study objective.  If this were a clinical trial with two drug arms and a placebo arm, it would be foolish to use one of the treatments as the reference group, particularly if you wanted to compare the efficacy of the treatments.  In this particular case, there is no clear reference group, but since the FICU is so much smaller than the other two units, we will use it as the reference group.  Computing the odds ratio for MICU and SICU we get 4.13 and 3.63, respectively.  These are also very strong associations, meaning that the odds of dying in the SICU and MICU are around 4 times higher than in the FICU, but relatively similar.\n",
    "\n",
    "Contingency tables and 2x2 tables in particular are the building blocks of working with binary data, and it's often a good way to begin looking at the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df860924",
   "metadata": {},
   "source": [
    "### Introducing Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "078cf641",
   "metadata": {},
   "source": [
    "Let's fit this model, and see how this works using a real example.  We fit logistic regression very similarly to how we fit linear regression models, with a few exceptions.  First, we will use a new function called `glm`, which allows one to fit a class of models known as generalized linear models or GLMs.  The `glm` function works in much the same way the `lm` function does.  We need to specify a formula of the form: `outcome ~ covariates`, specify what dataset to use (in our case the `dat` data frame), and then specify the family.  For logistic regression `family='binomial'` will be our choice.  You can run the `summary` function, just like you did for `lm` and it produces output very similar to what `lm` did.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "01e25f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dat[\"day_28_flg\"] = np.where(dat[\"day_28_flg\"] == 0, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "92f22537",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_new = ep.ad.df_to_anndata(dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "d53e3a07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         Generalized Linear Model Regression Results                          \n",
      "==============================================================================================\n",
      "Dep. Variable:     ['day_28_flg[0]', 'day_28_flg[1]']   No. Observations:                 1776\n",
      "Model:                                            GLM   Df Residuals:                     1774\n",
      "Model Family:                                Binomial   Df Model:                            1\n",
      "Link Function:                                  Logit   Scale:                          1.0000\n",
      "Method:                                          IRLS   Log-Likelihood:                -674.34\n",
      "Date:                                Wed, 05 Feb 2025   Deviance:                       1348.7\n",
      "Time:                                        09:49:24   Pearson chi2:                 1.78e+03\n",
      "No. Iterations:                                     6   Pseudo R-squ. (CS):             0.1111\n",
      "Covariance Type:                            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept         -3.0944      0.162    -19.142      0.000      -3.411      -2.778\n",
      "age_cat[T.>55]     2.1740      0.179     12.175      0.000       1.824       2.524\n",
      "==================================================================================\n"
     ]
    }
   ],
   "source": [
    "formula = \"day_28_flg ~ age_cat\"\n",
    "var_names = [\"day_28_flg\", \"age_cat\"]\n",
    "\n",
    "age_glm = ep.tl.glm(adata_new, var_names, formula, family=\"Binomial\", missing=\"drop\")\n",
    "age_glm_result = age_glm.fit()\n",
    "print(age_glm_result.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ced2f21",
   "metadata": {},
   "source": [
    "As you can see, we get a coefficients table that is similar to the `lm` table we used earlier.  Instead of a `t value`, we get a `z value`, but this can be interpreted similarly.  The rightmost column is a p-value, for testing the null hypothesis $\\beta=0$.  If you recall, the non-intercept coefficients are log-odds ratios, so testing if they are zero is equivalent to testing if the odds ratios are one.  If an odds ratio is one the odds are equal in the numerator group and denominator group, indicating the probabilities of the outcome are equal in each group.  So, assessing if the coefficients are zero will be an important aspect of doing this type of analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80414830",
   "metadata": {},
   "source": [
    "#### Beyond a Single Binary Covariate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb0bb1a4",
   "metadata": {},
   "source": [
    "While the above analysis is useful for illustration, it does not readily demonstrate anything we could not do with our 2x2 table example above.  Logistic regression allows us to extend the basic idea to at least two very relevant areas.  The first is the case where we have more than one covariate of interest.  Perhaps we have a confounder we are concerned about, and want to adjust for it.  Alternatively, maybe there are two covariates of interest.  Secondly, it allows use to use covariates as continuous quantities, instead of discretizing them into categories.  For example, instead of dividing age up into exhaustive strata (as we did very simply by just dividing the patients into two groups, $\\le55$ and $>55$ ), we could instead use age as a continuous covariate.\n",
    "\n",
    "First, having more than one covariate is simple.  For example, if we wanted to add `service_unit` to our previous model, we could just add it as we did when using the `lm` function for linear regression.  Here we specify `day_28_flg ~ age.cat + service_unit` and run the `summary` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "d16bb7cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         Generalized Linear Model Regression Results                          \n",
      "==============================================================================================\n",
      "Dep. Variable:     ['day_28_flg[0]', 'day_28_flg[1]']   No. Observations:                 1776\n",
      "Model:                                            GLM   Df Residuals:                     1772\n",
      "Model Family:                                Binomial   Df Model:                            3\n",
      "Link Function:                                  Logit   Scale:                          1.0000\n",
      "Method:                                          IRLS   Log-Likelihood:                -671.87\n",
      "Date:                                Wed, 05 Feb 2025   Deviance:                       1343.7\n",
      "Time:                                        09:49:24   Pearson chi2:                 1.74e+03\n",
      "No. Iterations:                                     6   Pseudo R-squ. (CS):             0.1136\n",
      "Covariance Type:                            nonrobust                                         \n",
      "========================================================================================\n",
      "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "Intercept               -4.2090      0.623     -6.761      0.000      -5.429      -2.989\n",
      "age_cat[T.>55]           2.1611      0.179     12.088      0.000       1.811       2.512\n",
      "service_unit[T.MICU]     1.1789      0.615      1.915      0.055      -0.027       2.385\n",
      "service_unit[T.SICU]     1.1234      0.614      1.830      0.067      -0.080       2.326\n",
      "========================================================================================\n"
     ]
    }
   ],
   "source": [
    "formula = \"day_28_flg ~ age_cat + service_unit\"\n",
    "var_names = [\"day_28_flg\", \"age_cat\", \"service_unit\"]\n",
    "\n",
    "ageunit_glm = ep.tl.glm(\n",
    "    adata_new, var_names, formula, family=\"Binomial\", missing=\"drop\"\n",
    ")\n",
    "ageunit_glm_result = ageunit_glm.fit()\n",
    "print(ageunit_glm_result.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67e140ed",
   "metadata": {},
   "source": [
    "A coefficient table is produced, and now we have four estimated coefficients.  The same two, `(Intercept)` and `age_cat[T.>55]` which were estimated in the unadjusted model, but also we have `service_unit[T.MICU]` and `service_unit[T.SICU]` which correspond to the log odds ratios for the MICU and SICU relative to the FICU.  Taking the exponential of these will result in an odds ratio for each variable, adjusted for the other variables in the model.  In this case the adjusted odds ratios for Age>55, MICU and SICU are 8.68, 3.25, and 3.08, respectively. We would conclude that there is an almost 9-fold increase in the odds of 28 day mortality for those in the $>55$ year age group relative to the younger $\\le55$ group while holding service unit constant.  This adjustment becomes important in many scenarios where groups of patients may be more or less likely to receive treatment, but also more or less likely to have better outcomes, where one effect is confounded by possibly many others.  Such is almost always the case with observational data, and this is why logistic regression is such a powerful data analysis tool in this setting.\n",
    "\n",
    "Another case we would like to be able to deal with is when we have a continuous covariate we would like to include in the model.  One can always break the continuous covariate into mutually exclusive categories by selecting break or cut points, but selecting the number and location of these points can be arbitrary, and in many cases unnecessary or inefficient.  Recall that in logistic regression we are fitting a model:\n",
    "\n",
    "\n",
    "$logit(p_x) = log(Odds_x) = log(\\frac{p_x}{1-p_x}) = \\beta_0 + \\beta_1 \\times x$,\n",
    "\n",
    "\n",
    "but now assume $x$ is continuous.  Imagine a hypothetical scenario where you know $\\beta_0$ and $\\beta_1$ and have a group of 50 year olds, and a group of 51 year olds.  The difference in the log Odds between the two groups is:\n",
    "\n",
    "\n",
    "$log(Odds_{51}) -log(Odds_{50}) = (\\beta_0 + \\beta_1 \\times 51) - (\\beta_0 + \\beta_1 \\times 50) = \\beta_1(51-50) = \\beta_1$.\n",
    "\n",
    "Hence, the odds ratio for 51 year olds versus 50 year olds is $\\exp{(\\beta_1)}$.  This is actually true for any group of patients which are 1 year apart, and this gives a useful way to interpret and use these estimated coefficients for continuous covariates.  Let's work with an example.  Again fitting the 28 day mortality outcome as a function of age, but treating age as it was originally recorded in the dataset, a continuous variable called `age`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "ca2d894c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         Generalized Linear Model Regression Results                          \n",
      "==============================================================================================\n",
      "Dep. Variable:     ['day_28_flg[0]', 'day_28_flg[1]']   No. Observations:                 1776\n",
      "Model:                                            GLM   Df Residuals:                     1774\n",
      "Model Family:                                Binomial   Df Model:                            1\n",
      "Link Function:                                  Logit   Scale:                          1.0000\n",
      "Method:                                          IRLS   Log-Likelihood:                -625.49\n",
      "Date:                                Wed, 05 Feb 2025   Deviance:                       1251.0\n",
      "Time:                                        09:49:24   Pearson chi2:                 1.81e+03\n",
      "No. Iterations:                                     6   Pseudo R-squ. (CS):             0.1587\n",
      "Covariance Type:                            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -5.7780      0.321    -18.013      0.000      -6.407      -5.149\n",
      "age            0.0652      0.004     14.595      0.000       0.056       0.074\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "formula = \"day_28_flg ~ age\"\n",
    "var_names = [\"day_28_flg\", \"age\"]\n",
    "agects_glm = ep.tl.glm(\n",
    "    adata_new,\n",
    "    var_names,\n",
    "    formula,\n",
    "    family=\"Binomial\",\n",
    "    missing=\"drop\",\n",
    "    as_continuous=[\"age\"],\n",
    ")\n",
    "agects_glm_result = agects_glm.fit()\n",
    "print(agects_glm_result.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5120994",
   "metadata": {},
   "source": [
    "We see the estimated coefficient is 0.07 and still very statistically significant.  Exponentiating the log odds ratio for age, we get an estimated odds ratio of 1.07, which is per 1 year increase in age.  What if the age difference of interest is ten years instead of one year?  There are at least two ways of doing this.  One is to replace `age` with `I(age/10)`, which uses a new covariate which is `age` divided by ten.  The second is to use the `agects.glm` estimated log odds ratio, and multiple by ten prior to exponentiating.  They will yield equivalent estimates of 1.92, but it is now per 10 year increases in age.  This is useful when the estimated odds ratios (or log odds ratios) are close to one (or zero).  When this is done, one unit of the covariate is 10 years, so the generic interpretation of the coefficients remains the same, but the units (per 10 years instead of per 1 year) changes.\n",
    "\n",
    "This of course assumes that the form of our equation relating the log odds of the outcome to the covariate is correct.  In cases where odds of the outcome decreases and increases as a function of the covariate, it is possible to estimate a relatively small effect of the linear covariate, when the outcome may be strongly affected by the covariate, but not in the way the model is specified.  Assessing the linearity of the log odds of the outcome and some discretized form of the covariate can be done graphically.  For instance, we can break age into 5 groups, and estimate the log odds of 28 day mortality in each group."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47037805",
   "metadata": {},
   "source": [
    "### Hypothesis Testing and Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6361a01",
   "metadata": {},
   "source": [
    "As was the case when using `lm`, we first fit the two competing models, a larger (alternative model), and a smaller (null model).  Provided that the models are nested, our larger model is the one which contained `service_unit` and `age.cat`, and the smaller only contains `age.cat`, so they are nested.  We are then testing if the log odds ratios for the two coefficients associated with `service_unit` are zero.  Let's call these coefficients $\\beta_{MICU}$ and $\\beta_{SICU}$.  To test if $\\beta_{MICU}$ and $\\beta_{SICU} = 0$, we can perform the following test:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "d4220ae6",
   "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>Model</th>\n",
       "      <th>formula</th>\n",
       "      <th>Df Resid.</th>\n",
       "      <th>Dev.</th>\n",
       "      <th>Df_diff</th>\n",
       "      <th>Pr(&gt;Chi)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>day_28_flg ~ age</td>\n",
       "      <td>1774</td>\n",
       "      <td>1348.676905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>day_28_flg ~ age + service_unit</td>\n",
       "      <td>1772</td>\n",
       "      <td>1343.745416</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.085237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Model                          formula  Df Resid.         Dev.  Df_diff  \\\n",
       "0      1                 day_28_flg ~ age       1774  1348.676905      NaN   \n",
       "1      2  day_28_flg ~ age + service_unit       1772  1343.745416      2.0   \n",
       "\n",
       "   Pr(>Chi)  \n",
       "0       NaN  \n",
       "1  0.085237  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ep.tl.anova_glm(\n",
    "    age_glm_result,\n",
    "    ageunit_glm_result,\n",
    "    \"day_28_flg ~ age\",\n",
    "    \"day_28_flg ~ age + service_unit\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14b7e397",
   "metadata": {},
   "source": [
    "A couple good practices to get in a habit are to first make sure the two competing models are correctly specified.  Here we are are testing `~ age.cat` versus `age.cat + service_unit`.  Next, the difference between the residual degrees of freedom (`Resid. Df`) in the two models tell us how many more parameters the larger model has when compared to the smaller model.  Here we see `1774 - 1772 = 2` which means that there are two more coefficients estimated in the larger model than the smaller one, which corresponds with the output from the `summary` table above.  Next looking at the p-value (`Pr(>Chi)`), we see a test for $\\beta_{MICU}$ and $\\beta_{SICU} = 0$ has a p-value of around 0.08.  At the typical 0.05 significance level, we would not reject the null, and use the simpler model without the service unit.  In logistic regression, this is a common way of testing whether a categorical covariate should be retained in the model, as it can be difficult to assess using the `z value` in the `summary` table, particularly when one is very statistically significant, and one is not."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52486768",
   "metadata": {},
   "source": [
    "### Confidence Intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "759ae2de",
   "metadata": {},
   "source": [
    "Generating confidence intervals for either the log-odds ratios or the odds ratios are relatively straightforward.  To get the log-odds ratios and respective confidence intervals for the `ageunit_glm` model which includes both age and service unit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f44164f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept              -4.209014\n",
       "age_cat[T.>55]          2.161142\n",
       "service_unit[T.MICU]    1.178866\n",
       "service_unit[T.SICU]    1.123443\n",
       "dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ageunit_glm_result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "eb4e3804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>-5.429257</td>\n",
       "      <td>-2.988771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.&gt;55]</th>\n",
       "      <td>1.810733</td>\n",
       "      <td>2.511552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service_unit[T.MICU]</th>\n",
       "      <td>-0.027452</td>\n",
       "      <td>2.385184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service_unit[T.SICU]</th>\n",
       "      <td>-0.079611</td>\n",
       "      <td>2.326497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0         1\n",
       "Intercept            -5.429257 -2.988771\n",
       "age_cat[T.>55]        1.810733  2.511552\n",
       "service_unit[T.MICU] -0.027452  2.385184\n",
       "service_unit[T.SICU] -0.079611  2.326497"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ageunit_glm_result.conf_int(alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089cef49",
   "metadata": {},
   "source": [
    "Here the coefficient estimates and confidence intervals are presented in much the same way as for a linear regression. In logistic regression, it is often convenient to exponentiate these quantities to get it on a more interpretable scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "13f46ac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept               0.014861\n",
       "age_cat[T.>55]          8.681049\n",
       "service_unit[T.MICU]    3.250686\n",
       "service_unit[T.SICU]    3.075425\n",
       "dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(ageunit_glm_result.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e5ae8a60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.003681</td>\n",
       "      <td>0.059992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_cat[T.&gt;55]</th>\n",
       "      <td>5.814857</td>\n",
       "      <td>12.960011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service_unit[T.MICU]</th>\n",
       "      <td>0.818181</td>\n",
       "      <td>12.915179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service_unit[T.SICU]</th>\n",
       "      <td>0.776964</td>\n",
       "      <td>12.173333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0          1\n",
       "Intercept             0.003681   0.059992\n",
       "age_cat[T.>55]        5.814857  12.960011\n",
       "service_unit[T.MICU]  0.818181  12.915179\n",
       "service_unit[T.SICU]  0.776964  12.173333"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(ageunit_glm_result.conf_int(alpha=0.025))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4774540e",
   "metadata": {},
   "source": [
    "Similar to linear regression, we will check whether the confidence intervals for the log odds ratios include zero.  This is equivalent to seeing if the intervals for the odds ratios include 1.  Since the odds ratios are more directly interpretable it is often more convenient to report them instead of the coefficients on the log odds ratio scale."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55fac1de",
   "metadata": {},
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85064f03",
   "metadata": {},
   "source": [
    "Let's first create a new data frame called `newdat` which computes all combinations of the values of variables passed to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "1bae70f7",
   "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_cat</th>\n",
       "      <th>service_unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt;=55</td>\n",
       "      <td>FICU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&lt;=55</td>\n",
       "      <td>MICU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&lt;=55</td>\n",
       "      <td>SICU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&gt;55</td>\n",
       "      <td>FICU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&gt;55</td>\n",
       "      <td>MICU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>&gt;55</td>\n",
       "      <td>SICU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age_cat service_unit\n",
       "0    <=55         FICU\n",
       "1    <=55         MICU\n",
       "2    <=55         SICU\n",
       "3     >55         FICU\n",
       "4     >55         MICU\n",
       "5     >55         SICU"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def expand_grid(dictionary):\n",
    "    return pd.DataFrame(\n",
    "        [row for row in product(*dictionary.values())], columns=dictionary.keys()\n",
    "    )\n",
    "\n",
    "\n",
    "dictionary = {\"age_cat\": [\"<=55\", \">55\"], \"service_unit\": [\"FICU\", \"MICU\", \"SICU\"]}\n",
    "\n",
    "newdat = expand_grid(dictionary)\n",
    "newdat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "2113471e",
   "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_cat</th>\n",
       "      <th>service_unit</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt;=55</td>\n",
       "      <td>FICU</td>\n",
       "      <td>0.014643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&lt;=55</td>\n",
       "      <td>MICU</td>\n",
       "      <td>0.046082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&lt;=55</td>\n",
       "      <td>SICU</td>\n",
       "      <td>0.043706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&gt;55</td>\n",
       "      <td>FICU</td>\n",
       "      <td>0.114268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&gt;55</td>\n",
       "      <td>MICU</td>\n",
       "      <td>0.295461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>&gt;55</td>\n",
       "      <td>SICU</td>\n",
       "      <td>0.284056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age_cat service_unit      pred\n",
       "0    <=55         FICU  0.014643\n",
       "1    <=55         MICU  0.046082\n",
       "2    <=55         SICU  0.043706\n",
       "3     >55         FICU  0.114268\n",
       "4     >55         MICU  0.295461\n",
       "5     >55         SICU  0.284056"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdat[\"pred\"] = ageunit_glm_result.predict(newdat)\n",
    "newdat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01abab1b",
   "metadata": {},
   "source": [
    "## Survival Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a45af782",
   "metadata": {},
   "source": [
    "### correct the observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "d76f007b",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_subset = adata[\n",
    "    :, [\"mort_day_censored\", \"censor_flg\", \"gender_num\", \"service_unit\"]\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d84b1ef",
   "metadata": {},
   "source": [
    "Normally, you can skip the next step, but since this dataset was used to analyze\n",
    "the data in a slightly different way, we need to correct the observation times for a\n",
    "subset of the subjects in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "fc657322",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_subset[:, [\"mort_day_censored\"]].X[adata_subset[:, [\"censor_flg\"]].X == 1] = 731"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bebcb14",
   "metadata": {},
   "source": [
    "### Kaplan-Meier Survival Curves"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40c21c9f",
   "metadata": {},
   "source": [
    "Kaplan-Meier plots are one of the most widely used plots in medical research. We can use `ehrapy.tools.kmp` (`ep.tl.kmp`), which internally calls KaplanMeierFitter, to fit the Kaplan-Meier estimate for the survival function. The function normally takes two arguments: a vector of times, and some kind of indicator for which patients had an event (death in our case). In our case, the vector of death and censoring times are the mort_day_censored, and deaths are coded with a zero in the censor_flg variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b370373",
   "metadata": {},
   "source": [
    "Note that in the MIMIC-II database, `censor_fl` is censored or death (binary: 0 = death, 1 = censored). <br /> In KaplanMeierFitter, `event_observed` is True if the the death was observed, False if the event was lost (right-censored). <br /> So we need to flip `censor_fl` when pass `censor_fl` to KaplanMeierFitter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "78ad7e16",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_subset[:, [\"censor_flg\"]].X = np.where(\n",
    "    adata_subset[:, [\"censor_flg\"]].X == 0, 1, 0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5b3e5f8",
   "metadata": {},
   "source": [
    "Fitting a Kaplan-Meier curve is quite easy, and we want to ‘fit’ by gender (gender_num). The default alpha value of KaplanMeierFitter is 0.05, which is 95 % confidence intervals for the survival functions. Lastly we add a legend, coding black for the women and red for the men."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "29f39b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = adata_subset[:, [\"mort_day_censored\"]].X\n",
    "E = adata_subset[:, [\"censor_flg\"]].X\n",
    "groups = adata_subset[:, [\"gender_num\"]].X\n",
    "adata_group_1 = adata_subset[groups == 0]\n",
    "adata_group_2 = adata_subset[groups == 1]\n",
    "kmf_1 = ep.tl.kaplan_meier(adata_group_1, \"mort_day_censored\", \"censor_flg\", label=\"Women\")\n",
    "kmf_2 = ep.tl.kaplan_meier(adata_group_2, \"mort_day_censored\", \"censor_flg\", label=\"Men\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "0a56f14b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ep.pl.kaplan_meier(\n",
    "    [kmf_1, kmf_2],\n",
    "    color=[\"k\", \"r\"],\n",
    "    xlim=[0, 750],\n",
    "    ylim=[0, 1],\n",
    "    xlabel=\"Days\",\n",
    "    ylabel=\"Proportion Who Survived\",\n",
    "    show=True,\n",
    "    display_survival_statistics=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9deba3f",
   "metadata": {},
   "source": [
    "As the above figure shows, there appears to be a difference between the survival function between the two gender groups, with again the male group (red) dying at slightly slower rate than the female group (black). We have included 95 % point-wise confidence bands for the survival function estimate, which assesses how much certain we are about the estimated survivorship at each point in time.\n",
    "\n",
    "We can do the same for `service_unit`, but since it has three groups, we need to change the color argument and legend to ensure the plot is properly labelled. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "dce0678e",
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = adata_subset[:, [\"service_unit\"]].X\n",
    "adata_group_ficu = adata_subset[groups == \"FICU\"]\n",
    "adata_group_micu = adata_subset[groups == \"MICU\"]\n",
    "adata_group_sicu = adata_subset[groups == \"SICU\"]\n",
    "kmf_1 = ep.tl.kaplan_meier(adata_group_ficu, \"mort_day_censored\", \"censor_flg\", label=\"FICU\")\n",
    "kmf_2 = ep.tl.kaplan_meier(adata_group_micu, \"mort_day_censored\", \"censor_flg\", label=\"MICU\")\n",
    "kmf_3 = ep.tl.kaplan_meier(adata_group_sicu, \"mort_day_censored\", \"censor_flg\", label=\"SICU\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "5b90b2fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ep.pl.kaplan_meier(\n",
    "    [kmf_1, kmf_2, kmf_3],\n",
    "    ci_show=[False, False, False],\n",
    "    color=[\"k\", \"r\", \"g\"],\n",
    "    xlim=[0, 750],\n",
    "    ylim=[0, 1],\n",
    "    xlabel=\"Days\",\n",
    "    ylabel=\"Proportion Who Survived\",\n",
    "    show=True,\n",
    "    display_survival_statistics=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1926ff",
   "metadata": {},
   "source": [
    "### Cox Proportional Hazards Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c529cf1",
   "metadata": {},
   "source": [
    "The most popular approach for modelling time to event outcomes in health data is likely the Cox Proportional Hazards Model, which is also sometimes called the Cox model or Cox Regression. As the name implies this method models something called the hazard function.\n",
    "\n",
    "The hazard function is a function of time (hours, days, years) and is approximately the instantaneous probability of the event occurring (i.e., chance the event is happening in some very small time window) given the event has not already happened."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "ad50558f",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_subset = adata_subset[:, [\"mort_day_censored\", \"censor_flg\", \"gender_num\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f894a29b",
   "metadata": {},
   "source": [
    "The CoxPHFitter function (from the lifelines package) is the fitting function for Cox models. In our case, let’s continue our example of using gender (`gender_num`) to model the datSurv outcome we created, and running the summary function to see what information is outputted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "dec473d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ArrayView([[11.92],\n",
       "           [731.0],\n",
       "           [731.0],\n",
       "           ...,\n",
       "           [62.21],\n",
       "           [234.13],\n",
       "           [731.0]], dtype=object)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_subset[:, [\"mort_day_censored\"]].X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb2caf51",
   "metadata": {},
   "source": [
    "Because `CoxPHFitter.fit()` needs a Pandas DataFrame as input, we have to convert AnnData to DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "bb3309fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ep.ad.anndata_to_df(adata_subset)\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "25d3bcb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_coxph = ep.tl.cox_ph(adata_subset, duration_col=\"mort_day_censored\", event_col=\"censor_flg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "e33bc61c",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'mort_day_censored'</td>\n",
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       "      <th>event col</th>\n",
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       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
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       "      <th>number of observations</th>\n",
       "      <td>1775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-3636.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2025-02-05 08:49:24 UTC</td>\n",
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       "</div><table border=\"1\" class=\"dataframe\">\n",
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       "    <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",
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       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
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       "      <th>gender_num</th>\n",
       "      <td>-0.29</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.24</td>\n",
       "      <td>&lt;0.005</td>\n",
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       "      <th>Concordance</th>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>7274.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>10.43 on 1 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>9.65</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) \\\\\n",
       "covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "gender_num & -0.29 & 0.75 & 0.09 & -0.47 & -0.11 & 0.63 & 0.89 & 0.00 & -3.24 & 0.00 & 9.71 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 1775 total observations, 1278 right-censored observations>\n",
       "             duration col = 'mort_day_censored'\n",
       "                event col = 'censor_flg'\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 1775\n",
       "number of events observed = 497\n",
       "   partial log-likelihood = -3636.11\n",
       "         time fit was run = 2025-02-05 08:49:24 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",
       "gender_num -0.29      0.75      0.09           -0.47           -0.11                0.63                0.89\n",
       "\n",
       "            cmp to     z      p  -log2(p)\n",
       "covariate                                \n",
       "gender_num    0.00 -3.24 <0.005      9.71\n",
       "---\n",
       "Concordance = 0.54\n",
       "Partial AIC = 7274.23\n",
       "log-likelihood ratio test = 10.43 on 1 df\n",
       "-log2(p) of ll-ratio test = 9.65"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_coxph.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "940fc860",
   "metadata": {},
   "source": [
    "The coefficients table has the familiar format, which we’ve seen before. The\n",
    "coef for `gender_num` is about −0.29, and this is the estimate of our log-hazard\n",
    "ratio. As discussed, taking the exponential of this gives the hazard ratio (HR),\n",
    "which the summary output computes in the next column (exp(coef)). Here, the\n",
    "HR is estimated at 0.75, indicating that men have about a 25 % reduction in the\n",
    "hazards of death, under the proportional hazards assumption."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f268730",
   "metadata": {},
   "source": [
    "The next column in the coefficient table has the standard error for the log hazard\n",
    "ratio, followed by the z score and p-value (Pr(>|z|)), which is very similar to\n",
    "what we saw in the case of logistic regression. Here we see the p-value is quite\n",
    "small, and we would reject the null hypothesis that the hazard functions are the\n",
    "same between men and women. This is consistent with the exploratory figures we\n",
    "produced using Kaplan-Meier curves in the previous section."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a62587",
   "metadata": {},
   "source": [
    "For CoxPHFitter, the\n",
    "summary function also conveniently outputs the confidence interval of the HR a\n",
    "few lines down, and here our estimate of the HR is 0.75 (95 % CI: 0.63–0.89,\n",
    "p = 0.001). This is how the HR would typically be reported."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf8e565e",
   "metadata": {},
   "source": [
    "Using more than one covariate works the same as our other analysis techniques.\n",
    "Adding a co-morbidity to the model such as atrial fibrillation (`afib_flg`) can be\n",
    "done as you would do for logistic regression. Because fit a Cox Proportional Hazards Models using CoxPHFitter requires a Pandas DataFrame as input, we also need to create a DataFrame from AnnData."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "22fb2860",
   "metadata": {},
   "outputs": [
    {
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       "      <td>'mort_day_censored'</td>\n",
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       "      <td>-3567.43</td>\n",
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       "      <td>2025-02-05 08:49:25 UTC</td>\n",
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       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
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       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gender_num</th>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>7.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>afib_flg</th>\n",
       "      <td>1.34</td>\n",
       "      <td>3.84</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.54</td>\n",
       "      <td>3.14</td>\n",
       "      <td>4.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.18</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>129.37</td>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>7138.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>147.80 on 2 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>106.62</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) \\\\\n",
       "covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "gender_num & -0.26 & 0.77 & 0.09 & -0.44 & -0.08 & 0.65 & 0.92 & 0.00 & -2.88 & 0.00 & 7.99 \\\\\n",
       "afib_flg & 1.34 & 3.84 & 0.10 & 1.14 & 1.54 & 3.14 & 4.68 & 0.00 & 13.18 & 0.00 & 129.37 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 1775 total observations, 1278 right-censored observations>\n",
       "             duration col = 'mort_day_censored'\n",
       "                event col = 'censor_flg'\n",
       "      baseline estimation = breslow\n",
       "   number of observations = 1775\n",
       "number of events observed = 497\n",
       "   partial log-likelihood = -3567.43\n",
       "         time fit was run = 2025-02-05 08:49:25 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",
       "gender_num -0.26      0.77      0.09           -0.44           -0.08                0.65                0.92\n",
       "afib_flg    1.34      3.84      0.10            1.14            1.54                3.14                4.68\n",
       "\n",
       "            cmp to     z      p  -log2(p)\n",
       "covariate                                \n",
       "gender_num    0.00 -2.88 <0.005      7.99\n",
       "afib_flg      0.00 13.18 <0.005    129.37\n",
       "---\n",
       "Concordance = 0.61\n",
       "Partial AIC = 7138.85\n",
       "log-likelihood ratio test = 147.80 on 2 df\n",
       "-log2(p) of ll-ratio test = 106.62"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata_subset = adata[:, [\"mort_day_censored\", \"censor_flg\", \"gender_num\", \"afib_flg\"]]\n",
    "\n",
    "genderafib_coxph = ep.tl.cox_ph(adata_subset, duration_col=\"mort_day_censored\", event_col=\"censor_flg\")\n",
    "genderafib_coxph.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb2a3ba1",
   "metadata": {},
   "source": [
    "Here again male gender is associated with reduced time to death, while atrial fibrillation increases the hazard of death by almost four-fold. Both are statistically significant in the summary output.\n",
    "\n",
    "Cox regression also allows one to use covariates which change over time. This would allow one to incorporate changes in treatment, disease severity, etc. within the same patient without need for any different methodology. The major challenge to do this is mainly in the construction of the dataset, which is discussed in some of the references at the end of this chapter. Some care is required when the time dependent covariate is only measured periodically, as the method requires that it be known at every event time for the entire cohort of patients, and not just those relevant to the patient in question. This is more practical for changes in treatment which may be recorded with some precision, particularly in a database like MIMIC II, and less so for laboratory results which may be measured at the resolution of hours, days or weeks. Interpolating between lab values or carrying the last observation forward has been shown to introduce several types of problems."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7119c2fb",
   "metadata": {},
   "source": [
    "### Caveats and Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54524c51",
   "metadata": {},
   "source": [
    "Survival analysis is distinguished from other forms of analyses covered in this Chapter, as it allows the data to be censored. As was the case for the other approaches we considered, there are modeling assumptions. For instance, it is important that the censoring is not informative of the survival time. For example, if censoring occurs when treatment is withdrawn because the patient is too sick to continue therapy, this would be an example of informative censoring. The validity of all methods discussed in this section are then invalid. Care should be taken to make sure you understand the censoring mechanism as to avoid any false inferences drawn.\n",
    "\n",
    "Assessment of the proportional hazards assumption is an important part of any Cox regression analysis. We refer you to the references at the end of this chapter for strategies and alternatives for when the proportional hazards assumption breaks down. In some circumstances, the proportional hazards assumption is not valid, and alternative approaches can be used. As is always the case, when outcomes are dependent (e.g., one patient may contribute more than one observation), the methods discussed in this section should not be used directly. Generally the standard error estimates will be too small, and p-values will be incorrect. The concerns in logistic regression regarding outliers, co-linearity, missing data, and covariates with sparse outcomes apply here as well, as do the concerns about model misspecification for continuous covariates."
   ]
  }
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