mimic_2_survival_analysis.ipynb

{
 "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": 146,
   "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": 147,
   "id": "d845678f-4473-48c1-9a9d-8dcc03ed41b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "3f45a5f8-409f-4c0d-802d-616473ae2810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----\n",
      "ehrapy              0.9.0\n",
      "rich                NA\n",
      "scanpy              1.10.4\n",
      "session_info        1.0.0\n",
      "-----\n",
      "PIL                 11.0.0\n",
      "anndata             0.11.1\n",
      "appnope             0.1.4\n",
      "asttokens           NA\n",
      "attr                24.2.0\n",
      "autograd            NA\n",
      "autograd_gamma      NA\n",
      "backports           NA\n",
      "cachetools          5.5.0\n",
      "causallearn         NA\n",
      "certifi             2024.08.30\n",
      "charset_normalizer  3.4.0\n",
      "click               8.1.7\n",
      "cloudpickle         3.1.0\n",
      "comm                0.2.2\n",
      "cycler              0.12.1\n",
      "cython_runtime      NA\n",
      "dask                2024.11.1\n",
      "dask_expr           1.1.18\n",
      "dask_ml             2024.4.4\n",
      "dateutil            2.9.0.post0\n",
      "db_dtypes           1.3.1\n",
      "debugpy             1.8.8\n",
      "decorator           5.1.1\n",
      "defusedxml          0.7.1\n",
      "dill                0.3.8\n",
      "distributed         2024.11.1\n",
      "dowhy               0.11.1\n",
      "executing           2.1.0\n",
      "fhiry               4.0.0\n",
      "filelock            3.16.1\n",
      "formulaic           1.0.2\n",
      "fsspec              2024.6.1\n",
      "google              NA\n",
      "graphlib            NA\n",
      "grpc                1.67.1\n",
      "grpc_status         NA\n",
      "h5py                3.12.1\n",
      "idna                3.10\n",
      "igraph              0.11.8\n",
      "imblearn            0.12.4\n",
      "interface_meta      1.3.0\n",
      "ipykernel           6.29.5\n",
      "ipywidgets          8.1.5\n",
      "jaraco              NA\n",
      "jedi                0.19.2\n",
      "jinja2              3.1.4\n",
      "joblib              1.4.2\n",
      "kiwisolver          1.4.7\n",
      "lamin_utils         0.13.7\n",
      "legacy_api_wrap     NA\n",
      "leidenalg           0.10.2\n",
      "lifelines           0.30.0\n",
      "llvmlite            0.43.0\n",
      "locket              NA\n",
      "markupsafe          3.0.2\n",
      "matplotlib          3.9.2\n",
      "matplotlib_inline   0.1.7\n",
      "missingno           0.5.2\n",
      "more_itertools      10.3.0\n",
      "mpl_toolkits        NA\n",
      "mpmath              1.3.0\n",
      "msgpack             1.1.0\n",
      "natsort             8.4.0\n",
      "networkx            3.4.2\n",
      "numba               0.60.0\n",
      "numpy               1.25.2\n",
      "packaging           24.2\n",
      "pandas              2.2.3\n",
      "parso               0.8.4\n",
      "patsy               1.0.1\n",
      "platformdirs        4.3.6\n",
      "prodict             NA\n",
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      "psutil              6.1.0\n",
      "pure_eval           0.2.3\n",
      "pyarrow             18.0.0\n",
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      "pyasn1_modules      0.4.1\n",
      "pydev_ipython       NA\n",
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      "pydevd              3.2.2\n",
      "pydevd_file_utils   NA\n",
      "pydevd_plugins      NA\n",
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      "pydot               3.0.2\n",
      "pygments            2.18.0\n",
      "pyparsing           3.2.0\n",
      "pytz                2024.2\n",
      "rapidfuzz           3.10.1\n",
      "requests            2.32.3\n",
      "rsa                 4.9\n",
      "scipy               1.9.3\n",
      "seaborn             0.13.2\n",
      "setuptools          74.1.2\n",
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      "six                 1.16.0\n",
      "sklearn             1.5.2\n",
      "sortedcontainers    2.4.0\n",
      "sparse              0.15.4\n",
      "sphinxcontrib       NA\n",
      "stack_data          0.6.3\n",
      "statsmodels         0.14.4\n",
      "sympy               1.13.1\n",
      "tableone            0.8.0\n",
      "tabulate            0.9.0\n",
      "tblib               3.0.0\n",
      "texttable           1.7.0\n",
      "thefuzz             0.22.1\n",
      "threadpoolctl       3.5.0\n",
      "timeago             1.0.14\n",
      "tlz                 1.0.0\n",
      "toolz               1.0.0\n",
      "torch               2.5.1\n",
      "torchgen            NA\n",
      "tornado             6.4.1\n",
      "tqdm                4.67.0\n",
      "traitlets           5.14.3\n",
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      "-----\n",
      "IPython             8.29.0\n",
      "jupyter_client      8.6.3\n",
      "jupyter_core        5.7.2\n",
      "-----\n",
      "Python 3.11.10 (main, Sep  7 2024, 01:03:31) [Clang 15.0.0 (clang-1500.3.9.4)]\n",
      "macOS-14.5-arm64-arm-64bit\n",
      "-----\n",
      "Session information updated at 2024-11-13 14:45\n"
     ]
    }
   ],
   "source": [
    "ep.print_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d01b06d-cd7a-4caf-8a41-e9ee2202e897",
   "metadata": {},
   "source": [
    "## MIMIC-II dataset preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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": 150,
   "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": 150,
     "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": 151,
   "id": "ed85cc55",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 152,
   "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": 153,
   "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, 13 Nov 2024</td> <th>  Prob (F-statistic):</th> <td>3.52e-108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:45:49</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, 13 Nov 2024 & \\textbf{  Prob (F-statistic):} & 3.52e-108   \\\\\n",
       "\\textbf{Time:}             &     14:45:49     & \\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, 13 Nov 2024   Prob (F-statistic):          3.52e-108\n",
       "Time:                        14:45:49   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": 153,
     "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": 154,
   "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, 13 Nov 2024</td> <th>  Prob (F-statistic):</th> <td>1.04e-106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:45:49</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, 13 Nov 2024 & \\textbf{  Prob (F-statistic):} & 1.04e-106   \\\\\n",
       "\\textbf{Time:}                  &     14:45:49     & \\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, 13 Nov 2024   Prob (F-statistic):          1.04e-106\n",
       "Time:                        14:45:49   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": 154,
     "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": 155,
   "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": 156,
   "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, 13 Nov 2024</td> <th>  Prob (F-statistic):</th> <td>7.60e-107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:45:49</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, 13 Nov 2024 & \\textbf{  Prob (F-statistic):} & 7.60e-107   \\\\\n",
       "\\textbf{Time:}             &     14:45:49     & \\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, 13 Nov 2024   Prob (F-statistic):          7.60e-107\n",
       "Time:                        14:45:49   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": 156,
     "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": 157,
   "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": 158,
   "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, 13 Nov 2024</td> <th>  Prob (F-statistic):</th> <td>5.09e-106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:45:49</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, 13 Nov 2024 & \\textbf{  Prob (F-statistic):} & 5.09e-106   \\\\\n",
       "\\textbf{Time:}                   &     14:45:49     & \\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, 13 Nov 2024   Prob (F-statistic):          5.09e-106\n",
       "Time:                        14:45:49   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": 158,
     "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": 159,
   "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": 160,
   "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": 160,
     "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": 161,
   "id": "4078d57f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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": 161,
     "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": 162,
   "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": 163,
   "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": 163,
     "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": 164,
   "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": 165,
   "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": 166,
   "id": "e3d29bbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_cat\n",
       "<=55    923\n",
       ">55     853\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 166,
     "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": 167,
   "id": "71ec4e6a",
   "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>&lt;=55</th>\n",
       "      <td>883</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;55</th>\n",
       "      <td>610</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0    1\n",
       "<=55  883   40\n",
       ">55   610  243"
      ]
     },
     "execution_count": 167,
     "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": 168,
   "id": "b5c9a6b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>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": 168,
     "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": 169,
   "id": "c43a435a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>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": 169,
     "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": 170,
   "id": "662a6bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FICU    0.050847\n",
       "MICU    0.209917\n",
       "SICU    0.184560\n",
       "dtype: float64"
      ]
     },
     "execution_count": 170,
     "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": 171,
   "id": "01e25f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dat[\"day_28_flg\"] = np.where(dat[\"day_28_flg\"] == 0, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "92f22537",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_new = ep.ad.df_to_anndata(dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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, 13 Nov 2024   Deviance:                       1348.7\n",
      "Time:                                        14:45:50   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": 174,
   "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, 13 Nov 2024   Deviance:                       1343.7\n",
      "Time:                                        14:45:50   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": 175,
   "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, 13 Nov 2024   Deviance:                       1251.0\n",
      "Time:                                        14:45:50   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": 176,
   "id": "d4220ae6",
   "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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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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": 176,
     "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": 177,
   "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": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ageunit_glm_result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "eb4e3804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        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": 178,
     "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": 179,
   "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": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(ageunit_glm_result.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "e5ae8a60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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": 180,
     "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": 181,
   "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": 181,
     "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": 182,
   "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": 182,
     "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": 183,
   "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": 184,
   "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": 185,
   "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": null,
   "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": 187,
   "id": "0a56f14b",
   "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.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",
    ")"
   ]
  },
  {
   "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": null,
   "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": 189,
   "id": "5b90b2fe",
   "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.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",
    ")"
   ]
  },
  {
   "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": 190,
   "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": 191,
   "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": 191,
     "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": 192,
   "id": "bb3309fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ep.ad.anndata_to_df(adata_subset)\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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": 194,
   "id": "e33bc61c",
   "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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'mort_day_censored'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'censor_flg'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>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>2024-11-13 13:45:50 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
       "      <td>9.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <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 = 2024-11-13 13:45:50 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": 195,
   "id": "22fb2860",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'mort_day_censored'</td>\n",
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       "      <th>event col</th>\n",
       "      <td>'censor_flg'</td>\n",
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       "      <td>breslow</td>\n",
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       "      <th>number of observations</th>\n",
       "      <td>1775</td>\n",
       "    </tr>\n",
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       "      <th>number of events observed</th>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
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       "      <th>partial log-likelihood</th>\n",
       "      <td>-3567.43</td>\n",
       "    </tr>\n",
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       "      <th>time fit was run</th>\n",
       "      <td>2024-11-13 13:45:50 UTC</td>\n",
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       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
       "  </tbody>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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 = 2024-11-13 13:45:50 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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