Ch11-surv-lab.ipynb

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   "source": [
    "# Survival Analysis\n",
    "\n",
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch11-surv-lab.ipynb\">\n",
    "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>\n",
    "\n",
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch11-surv-lab.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca5893e0",
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   "source": [
    " In this lab, we perform survival analyses on three separate data\n",
    "sets. In  Section~\\ref{brain.cancer.sec} we analyze the  `BrainCancer` \n",
    "data  that was first described in Section~\\ref{sec:KM}. In Section~\\ref{time.to.pub.sec}, we examine the  `Publication` \n",
    "data  from Section~\\ref{sec:pub}. Finally,  Section~\\ref{call.center.sec}  explores\n",
    "a simulated call-center data set.\n",
    "\n",
    "We begin by importing some of our libraries at this top\n",
    "level. This makes the code more readable, as scanning the first few\n",
    "lines of the notebook tell us what libraries are used in this\n",
    "notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80c238e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:54.685557Z",
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   "outputs": [],
   "source": [
    "from matplotlib.pyplot import subplots\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ISLP.models import ModelSpec as MS\n",
    "from ISLP import load_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b40107",
   "metadata": {},
   "source": [
    "We  also collect the new imports\n",
    "needed for this lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a236fb17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:55.551082Z",
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     "shell.execute_reply": "2024-06-04T23:19:55.640185Z"
    }
   },
   "outputs": [],
   "source": [
    "from lifelines import \\\n",
    "     (KaplanMeierFitter,\n",
    "      CoxPHFitter)\n",
    "from lifelines.statistics import \\\n",
    "     (logrank_test,\n",
    "      multivariate_logrank_test)\n",
    "from ISLP.survival import sim_time\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b607d160",
   "metadata": {},
   "source": [
    "## Brain Cancer Data\n",
    "\n",
    "We begin with the `BrainCancer` data set, contained in the `ISLP` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dd4ddba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:55.642112Z",
     "iopub.status.busy": "2024-06-04T23:19:55.642009Z",
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     "shell.execute_reply": "2024-06-04T23:19:55.647761Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sex', 'diagnosis', 'loc', 'ki', 'gtv', 'stereo', 'status', 'time'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BrainCancer = load_data('BrainCancer')\n",
    "BrainCancer.columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c02be52d",
   "metadata": {},
   "source": [
    "The rows index the 88 patients, while the 8 columns contain the predictors and outcome variables.\n",
    "We first briefly examine the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae9435b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:55.649419Z",
     "iopub.status.busy": "2024-06-04T23:19:55.649331Z",
     "iopub.status.idle": "2024-06-04T23:19:55.651992Z",
     "shell.execute_reply": "2024-06-04T23:19:55.651800Z"
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    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "Female    45\n",
       "Male      43\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BrainCancer['sex'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6e94032",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:55.653141Z",
     "iopub.status.busy": "2024-06-04T23:19:55.653065Z",
     "iopub.status.idle": "2024-06-04T23:19:55.655301Z",
     "shell.execute_reply": "2024-06-04T23:19:55.655136Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis\n",
       "Meningioma    42\n",
       "HG glioma     22\n",
       "Other         14\n",
       "LG glioma      9\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BrainCancer['diagnosis'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "afa05da1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:55.656326Z",
     "iopub.status.busy": "2024-06-04T23:19:55.656246Z",
     "iopub.status.idle": "2024-06-04T23:19:55.658308Z",
     "shell.execute_reply": "2024-06-04T23:19:55.658119Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "0    53\n",
       "1    35\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BrainCancer['status'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7129324",
   "metadata": {},
   "source": [
    "Before beginning an analysis, it is important to know how the\n",
    "`status` variable has been coded.  Most software\n",
    "uses the convention that a `status` of 1 indicates an\n",
    "uncensored observation (often death), and a `status` of 0 indicates a censored\n",
    "observation. But some scientists might use the opposite coding. For\n",
    "the  `BrainCancer`  data set 35 patients died before the end of\n",
    "the study, so we are using the conventional coding.\n",
    "\n",
    "To begin the analysis, we re-create  the Kaplan-Meier survival curve shown in Figure~\\ref{fig:survbrain}. The main\n",
    "package we will use for survival analysis\n",
    "is `lifelines`.\n",
    "The variable  `time`  corresponds to $y_i$, the time to the $i$th event (either censoring or\n",
    "death). The first argument to `km.fit` is the event time, and the\n",
    "second argument is the censoring variable, with a 1 indicating an observed\n",
    "failure time. The `plot()` method produces a survival curve with pointwise confidence\n",
    "intervals. By default, these are  90% confidence intervals, but this can be changed\n",
    "by setting the `alpha` argument to one minus the desired\n",
    "confidence level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34c7c534",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:55.659492Z",
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     "shell.execute_reply": "2024-06-04T23:19:55.845461Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='timeline'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize=(8,8))\n",
    "km = KaplanMeierFitter()\n",
    "km_brain = km.fit(BrainCancer['time'], BrainCancer['status'])\n",
    "km_brain.plot(label='Kaplan Meier estimate', ax=ax)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118e57ab",
   "metadata": {},
   "source": [
    "Next we create Kaplan-Meier survival curves that are stratified by\n",
    "`sex`, in order to reproduce  Figure~\\ref{fig:survbrain2}.\n",
    "We  do this using the `groupby()` method of  a  dataframe.\n",
    "This  method returns a generator that can\n",
    "be  iterated over in the `for` loop. In this case,\n",
    "the  items  in the `for` loop are  2-tuples  representing\n",
    "the groups: the first entry is the  value\n",
    "of the grouping column  `sex` while the  second  value\n",
    "is the dataframe consisting of all  rows in the\n",
    "dataframe matching that value of `sex`.\n",
    "We will want to use this data below\n",
    "in the log-rank test,  hence we store this\n",
    "information in the dictionary `by_sex`. Finally,\n",
    "we have also used the notion of\n",
    " *string interpolation* to automatically\n",
    "label the  different lines in the  plot. String\n",
    "interpolation is a powerful technique to format strings ---\n",
    "`Python` has many ways to facilitate such operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e742ebe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:55.849368Z",
     "iopub.status.busy": "2024-06-04T23:19:55.849075Z",
     "iopub.status.idle": "2024-06-04T23:19:55.960684Z",
     "shell.execute_reply": "2024-06-04T23:19:55.960431Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize=(8,8))\n",
    "by_sex = {}\n",
    "for sex, df in BrainCancer.groupby('sex'):\n",
    "    by_sex[sex] = df\n",
    "    km_sex = km.fit(df['time'], df['status'])\n",
    "    km_sex.plot(label='Sex=%s' % sex, ax=ax)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1575e8",
   "metadata": {},
   "source": [
    "As discussed in Section~\\ref{sec:logrank}, we can perform a\n",
    "log-rank test to compare the survival of males to females. We use\n",
    "the `logrank_test()` function from the `lifelines.statistics` module.\n",
    "The first two arguments are the event times, with the second\n",
    "denoting the corresponding (optional) censoring indicators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4106e397",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:55.962264Z",
     "iopub.status.busy": "2024-06-04T23:19:55.962134Z",
     "iopub.status.idle": "2024-06-04T23:19:56.004551Z",
     "shell.execute_reply": "2024-06-04T23:19:56.004317Z"
    },
    "lines_to_next_cell": 2
   },
   "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>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.44</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrr}\n",
       " & test_statistic & p & -log2(p) \\\\\n",
       "0 & 1.44 & 0.23 & 2.12 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.StatisticalResult: logrank_test>\n",
       "               t_0 = -1\n",
       " null_distribution = chi squared\n",
       "degrees_of_freedom = 1\n",
       "         test_name = logrank_test\n",
       "\n",
       "---\n",
       " test_statistic    p  -log2(p)\n",
       "           1.44 0.23      2.12"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logrank_test(by_sex['Male']['time'],\n",
    "             by_sex['Female']['time'],\n",
    "             by_sex['Male']['status'],\n",
    "             by_sex['Female']['status'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba727cb6",
   "metadata": {},
   "source": [
    "The resulting $p$-value is $0.23$, indicating no evidence of a\n",
    "difference in survival between the two sexes.\n",
    "\n",
    "Next, we  use the `CoxPHFitter()`  estimator\n",
    "from `lifelines` to fit Cox proportional hazards models.\n",
    "To begin, we consider a model that uses  `sex`  as the only predictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8962942d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.005821Z",
     "iopub.status.busy": "2024-06-04T23:19:56.005710Z",
     "iopub.status.idle": "2024-06-04T23:19:56.022741Z",
     "shell.execute_reply": "2024-06-04T23:19:56.022512Z"
    }
   },
   "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>coef</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sex[Male]</th>\n",
       "      <td>0.407668</td>\n",
       "      <td>0.342004</td>\n",
       "      <td>0.233262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               coef  se(coef)         p\n",
       "covariate                              \n",
       "sex[Male]  0.407668  0.342004  0.233262"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coxph = CoxPHFitter # shorthand\n",
    "sex_df = BrainCancer[['time', 'status', 'sex']]\n",
    "model_df = MS(['time', 'status', 'sex'],\n",
    "              intercept=False).fit_transform(sex_df)\n",
    "cox_fit = coxph().fit(model_df,\n",
    "                      'time',\n",
    "                      'status')\n",
    "cox_fit.summary[['coef', 'se(coef)', 'p']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc490686",
   "metadata": {},
   "source": [
    "The first argument to `fit` should be a data frame containing\n",
    "at least the event time (the second argument `time` in this case),\n",
    "as well as an optional censoring variable (the argument `status` in this case).\n",
    "Note also that the Cox model does not include an intercept, which is why\n",
    "we used the `intercept=False` argument to `ModelSpec` above.\n",
    "The `summary()` method delivers many columns; we chose to abbreviate its output here.\n",
    "It is possible to obtain the likelihood ratio test comparing this model to the one\n",
    "with no features as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d923fe3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.024051Z",
     "iopub.status.busy": "2024-06-04T23:19:56.023976Z",
     "iopub.status.idle": "2024-06-04T23:19:56.028225Z",
     "shell.execute_reply": "2024-06-04T23:19:56.027998Z"
    }
   },
   "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>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>log-likelihood ratio test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.44</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrr}\n",
       " & test_statistic & p & -log2(p) \\\\\n",
       "0 & 1.44 & 0.23 & 2.12 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.StatisticalResult: log-likelihood ratio test>\n",
       "null_distribution = chi squared\n",
       "  degrees_freedom = 1\n",
       "        test_name = log-likelihood ratio test\n",
       "\n",
       "---\n",
       " test_statistic    p  -log2(p)\n",
       "           1.44 0.23      2.12"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cox_fit.log_likelihood_ratio_test()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "154d9df8",
   "metadata": {},
   "source": [
    "Regardless of which test we use, we see that there is no clear\n",
    "evidence for a difference in survival between males and females.  As\n",
    "we learned in this chapter, the score test from the Cox model is\n",
    "exactly equal to the log rank test statistic!\n",
    "\n",
    "Now we fit a  model that makes use of additional predictors. We first note\n",
    "that one of our `diagnosis` values is missing, hence\n",
    "we drop that observation before continuing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43b23849",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.029525Z",
     "iopub.status.busy": "2024-06-04T23:19:56.029451Z",
     "iopub.status.idle": "2024-06-04T23:19:56.055622Z",
     "shell.execute_reply": "2024-06-04T23:19:56.055386Z"
    }
   },
   "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>coef</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sex[Male]</th>\n",
       "      <td>0.183748</td>\n",
       "      <td>0.360358</td>\n",
       "      <td>0.610119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diagnosis[LG glioma]</th>\n",
       "      <td>-1.239530</td>\n",
       "      <td>0.579555</td>\n",
       "      <td>0.032455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diagnosis[Meningioma]</th>\n",
       "      <td>-2.154566</td>\n",
       "      <td>0.450524</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diagnosis[Other]</th>\n",
       "      <td>-1.268870</td>\n",
       "      <td>0.617672</td>\n",
       "      <td>0.039949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc[Supratentorial]</th>\n",
       "      <td>0.441195</td>\n",
       "      <td>0.703669</td>\n",
       "      <td>0.530665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ki</th>\n",
       "      <td>-0.054955</td>\n",
       "      <td>0.018314</td>\n",
       "      <td>0.002693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gtv</th>\n",
       "      <td>0.034293</td>\n",
       "      <td>0.022333</td>\n",
       "      <td>0.124661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stereo[SRT]</th>\n",
       "      <td>0.177778</td>\n",
       "      <td>0.601578</td>\n",
       "      <td>0.767597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           coef  se(coef)         p\n",
       "covariate                                          \n",
       "sex[Male]              0.183748  0.360358  0.610119\n",
       "diagnosis[LG glioma]  -1.239530  0.579555  0.032455\n",
       "diagnosis[Meningioma] -2.154566  0.450524  0.000002\n",
       "diagnosis[Other]      -1.268870  0.617672  0.039949\n",
       "loc[Supratentorial]    0.441195  0.703669  0.530665\n",
       "ki                    -0.054955  0.018314  0.002693\n",
       "gtv                    0.034293  0.022333  0.124661\n",
       "stereo[SRT]            0.177778  0.601578  0.767597"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned = BrainCancer.dropna()\n",
    "all_MS = MS(cleaned.columns, intercept=False)\n",
    "all_df = all_MS.fit_transform(cleaned)\n",
    "fit_all = coxph().fit(all_df,\n",
    "                      'time',\n",
    "                      'status')\n",
    "fit_all.summary[['coef', 'se(coef)', 'p']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8914f60",
   "metadata": {},
   "source": [
    " The `diagnosis` variable has been coded so that the baseline\n",
    "corresponds to HG glioma. The results indicate that the risk associated with HG glioma\n",
    "is more than eight times (i.e. $e^{2.15}=8.62$) the risk associated\n",
    "with meningioma. In other words, after adjusting for the other\n",
    "predictors, patients with HG glioma have much worse survival compared\n",
    "to those with meningioma.  In addition, larger values of the Karnofsky\n",
    "index, `ki`, are associated with lower risk, i.e. longer survival.\n",
    "\n",
    "Finally, we plot estimated survival curves for each diagnosis category,\n",
    "adjusting for the other predictors.  To make these plots, we set the\n",
    "values of the other predictors equal to the mean for quantitative variables\n",
    "and equal to the mode for categorical. To do this, we use the\n",
    "`apply()` method across rows (i.e. `axis=0`) with a function\n",
    "`representative` that checks if a column is categorical\n",
    "or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "20f95f4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.056935Z",
     "iopub.status.busy": "2024-06-04T23:19:56.056860Z",
     "iopub.status.idle": "2024-06-04T23:19:56.059439Z",
     "shell.execute_reply": "2024-06-04T23:19:56.059190Z"
    }
   },
   "outputs": [],
   "source": [
    "levels = cleaned['diagnosis'].unique()\n",
    "def representative(series):\n",
    "    if hasattr(series.dtype, 'categories'):\n",
    "        return pd.Series.mode(series)\n",
    "    else:\n",
    "        return series.mean()\n",
    "modal_data = cleaned.apply(representative, axis=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c35898c9",
   "metadata": {},
   "source": [
    "We make four\n",
    "copies of the column means and assign the `diagnosis` column to be the four different\n",
    "diagnoses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "18ef52a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.060778Z",
     "iopub.status.busy": "2024-06-04T23:19:56.060701Z",
     "iopub.status.idle": "2024-06-04T23:19:56.065090Z",
     "shell.execute_reply": "2024-06-04T23:19:56.064856Z"
    }
   },
   "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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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>loc</th>\n",
       "      <th>ki</th>\n",
       "      <th>gtv</th>\n",
       "      <th>stereo</th>\n",
       "      <th>status</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>Meningioma</td>\n",
       "      <td>Supratentorial</td>\n",
       "      <td>80.91954</td>\n",
       "      <td>8.687011</td>\n",
       "      <td>SRT</td>\n",
       "      <td>0.402299</td>\n",
       "      <td>27.188621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>HG glioma</td>\n",
       "      <td>Supratentorial</td>\n",
       "      <td>80.91954</td>\n",
       "      <td>8.687011</td>\n",
       "      <td>SRT</td>\n",
       "      <td>0.402299</td>\n",
       "      <td>27.188621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>LG glioma</td>\n",
       "      <td>Supratentorial</td>\n",
       "      <td>80.91954</td>\n",
       "      <td>8.687011</td>\n",
       "      <td>SRT</td>\n",
       "      <td>0.402299</td>\n",
       "      <td>27.188621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>Other</td>\n",
       "      <td>Supratentorial</td>\n",
       "      <td>80.91954</td>\n",
       "      <td>8.687011</td>\n",
       "      <td>SRT</td>\n",
       "      <td>0.402299</td>\n",
       "      <td>27.188621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex   diagnosis             loc        ki       gtv stereo    status  \\\n",
       "0  Female  Meningioma  Supratentorial  80.91954  8.687011    SRT  0.402299   \n",
       "0  Female   HG glioma  Supratentorial  80.91954  8.687011    SRT  0.402299   \n",
       "0  Female   LG glioma  Supratentorial  80.91954  8.687011    SRT  0.402299   \n",
       "0  Female       Other  Supratentorial  80.91954  8.687011    SRT  0.402299   \n",
       "\n",
       "        time  \n",
       "0  27.188621  \n",
       "0  27.188621  \n",
       "0  27.188621  \n",
       "0  27.188621  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modal_df = pd.DataFrame(\n",
    "              [modal_data.iloc[0] for _ in range(len(levels))])\n",
    "modal_df['diagnosis'] = levels\n",
    "modal_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b940735",
   "metadata": {},
   "source": [
    "We then construct the model matrix based on the model specification `all_MS` used to fit\n",
    "the model, and name the rows according to the levels of `diagnosis`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dc826bbc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.066333Z",
     "iopub.status.busy": "2024-06-04T23:19:56.066235Z",
     "iopub.status.idle": "2024-06-04T23:19:56.073007Z",
     "shell.execute_reply": "2024-06-04T23:19:56.072802Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\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>sex[Male]</th>\n",
       "      <th>diagnosis[LG glioma]</th>\n",
       "      <th>diagnosis[Meningioma]</th>\n",
       "      <th>diagnosis[Other]</th>\n",
       "      <th>loc[Supratentorial]</th>\n",
       "      <th>ki</th>\n",
       "      <th>gtv</th>\n",
       "      <th>stereo[SRT]</th>\n",
       "      <th>status</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Meningioma</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.91954</td>\n",
       "      <td>8.687011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.402299</td>\n",
       "      <td>27.188621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HG glioma</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.91954</td>\n",
       "      <td>8.687011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.402299</td>\n",
       "      <td>27.188621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG glioma</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.91954</td>\n",
       "      <td>8.687011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.402299</td>\n",
       "      <td>27.188621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.91954</td>\n",
       "      <td>8.687011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.402299</td>\n",
       "      <td>27.188621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sex[Male]  diagnosis[LG glioma]  diagnosis[Meningioma]  \\\n",
       "Meningioma        0.0                   0.0                    1.0   \n",
       "HG glioma         0.0                   0.0                    0.0   \n",
       "LG glioma         0.0                   1.0                    0.0   \n",
       "Other             0.0                   0.0                    0.0   \n",
       "\n",
       "            diagnosis[Other]  loc[Supratentorial]        ki       gtv  \\\n",
       "Meningioma               0.0                  1.0  80.91954  8.687011   \n",
       "HG glioma                0.0                  1.0  80.91954  8.687011   \n",
       "LG glioma                0.0                  1.0  80.91954  8.687011   \n",
       "Other                    1.0                  1.0  80.91954  8.687011   \n",
       "\n",
       "            stereo[SRT]    status       time  \n",
       "Meningioma          1.0  0.402299  27.188621  \n",
       "HG glioma           1.0  0.402299  27.188621  \n",
       "LG glioma           1.0  0.402299  27.188621  \n",
       "Other               1.0  0.402299  27.188621  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modal_X = all_MS.transform(modal_df)\n",
    "modal_X.index = levels\n",
    "modal_X\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b38205e5",
   "metadata": {},
   "source": [
    "We can use the `predict_survival_function()` method to obtain the estimated survival function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "867045f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.074287Z",
     "iopub.status.busy": "2024-06-04T23:19:56.074217Z",
     "iopub.status.idle": "2024-06-04T23:19:56.079015Z",
     "shell.execute_reply": "2024-06-04T23:19:56.078796Z"
    },
    "lines_to_next_cell": 0
   },
   "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>Meningioma</th>\n",
       "      <th>HG glioma</th>\n",
       "      <th>LG glioma</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.07</th>\n",
       "      <td>0.997947</td>\n",
       "      <td>0.982430</td>\n",
       "      <td>0.994881</td>\n",
       "      <td>0.995029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.18</th>\n",
       "      <td>0.997947</td>\n",
       "      <td>0.982430</td>\n",
       "      <td>0.994881</td>\n",
       "      <td>0.995029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.41</th>\n",
       "      <td>0.995679</td>\n",
       "      <td>0.963342</td>\n",
       "      <td>0.989245</td>\n",
       "      <td>0.989555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.54</th>\n",
       "      <td>0.995679</td>\n",
       "      <td>0.963342</td>\n",
       "      <td>0.989245</td>\n",
       "      <td>0.989555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.03</th>\n",
       "      <td>0.995679</td>\n",
       "      <td>0.963342</td>\n",
       "      <td>0.989245</td>\n",
       "      <td>0.989555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65.02</th>\n",
       "      <td>0.688772</td>\n",
       "      <td>0.040136</td>\n",
       "      <td>0.394181</td>\n",
       "      <td>0.404936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67.38</th>\n",
       "      <td>0.688772</td>\n",
       "      <td>0.040136</td>\n",
       "      <td>0.394181</td>\n",
       "      <td>0.404936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73.74</th>\n",
       "      <td>0.688772</td>\n",
       "      <td>0.040136</td>\n",
       "      <td>0.394181</td>\n",
       "      <td>0.404936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78.75</th>\n",
       "      <td>0.688772</td>\n",
       "      <td>0.040136</td>\n",
       "      <td>0.394181</td>\n",
       "      <td>0.404936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82.56</th>\n",
       "      <td>0.688772</td>\n",
       "      <td>0.040136</td>\n",
       "      <td>0.394181</td>\n",
       "      <td>0.404936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Meningioma  HG glioma  LG glioma     Other\n",
       "0.07     0.997947   0.982430   0.994881  0.995029\n",
       "1.18     0.997947   0.982430   0.994881  0.995029\n",
       "1.41     0.995679   0.963342   0.989245  0.989555\n",
       "1.54     0.995679   0.963342   0.989245  0.989555\n",
       "2.03     0.995679   0.963342   0.989245  0.989555\n",
       "...           ...        ...        ...       ...\n",
       "65.02    0.688772   0.040136   0.394181  0.404936\n",
       "67.38    0.688772   0.040136   0.394181  0.404936\n",
       "73.74    0.688772   0.040136   0.394181  0.404936\n",
       "78.75    0.688772   0.040136   0.394181  0.404936\n",
       "82.56    0.688772   0.040136   0.394181  0.404936\n",
       "\n",
       "[85 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_survival = fit_all.predict_survival_function(modal_X)\n",
    "predicted_survival\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5696e707",
   "metadata": {},
   "source": [
    "This returns a data frame,\n",
    "whose plot methods yields the different survival curves. To avoid clutter in\n",
    "the plots, we do not display confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9fa60bac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.080399Z",
     "iopub.status.busy": "2024-06-04T23:19:56.080330Z",
     "iopub.status.idle": "2024-06-04T23:19:56.151344Z",
     "shell.execute_reply": "2024-06-04T23:19:56.151104Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize=(8, 8))\n",
    "predicted_survival.plot(ax=ax);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87e8e78f",
   "metadata": {},
   "source": [
    "## Publication Data\n",
    "The  `Publication`  data   presented in Section~\\ref{sec:pub}  can be\n",
    "found in the `ISLP` package.\n",
    "We first reproduce Figure~\\ref{fig:lauersurv}  by plotting the Kaplan-Meier curves\n",
    "stratified on the  `posres`  variable, which records whether the\n",
    "study had a positive or negative result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8c1a98e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.152821Z",
     "iopub.status.busy": "2024-06-04T23:19:56.152701Z",
     "iopub.status.idle": "2024-06-04T23:19:56.230052Z",
     "shell.execute_reply": "2024-06-04T23:19:56.229811Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize=(8,8))\n",
    "Publication = load_data('Publication')\n",
    "by_result = {}\n",
    "for result, df in Publication.groupby('posres'):\n",
    "    by_result[result] = df\n",
    "    km_result = km.fit(df['time'], df['status'])\n",
    "    km_result.plot(label='Result=%d' % result, ax=ax)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21bca65e",
   "metadata": {},
   "source": [
    "As discussed previously, the $p$-values from fitting Cox’s\n",
    "proportional hazards model to the `posres` variable are quite\n",
    "large, providing no evidence of a difference in time-to-publication\n",
    "between studies with positive versus negative results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cd1ed9ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.231448Z",
     "iopub.status.busy": "2024-06-04T23:19:56.231371Z",
     "iopub.status.idle": "2024-06-04T23:19:56.253732Z",
     "shell.execute_reply": "2024-06-04T23:19:56.253504Z"
    },
    "lines_to_next_cell": 2
   },
   "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>coef</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>posres</th>\n",
       "      <td>0.148076</td>\n",
       "      <td>0.161625</td>\n",
       "      <td>0.359579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               coef  se(coef)         p\n",
       "covariate                              \n",
       "posres     0.148076  0.161625  0.359579"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posres_df = MS(['posres',\n",
    "                'time',\n",
    "                'status'],\n",
    "                intercept=False).fit_transform(Publication)\n",
    "posres_fit = coxph().fit(posres_df,\n",
    "                         'time',\n",
    "                         'status')\n",
    "posres_fit.summary[['coef', 'se(coef)', 'p']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f00766",
   "metadata": {},
   "source": [
    "However, the results change dramatically when we include other\n",
    "predictors in the model. Here we exclude the funding mechanism\n",
    "variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d53335b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.255073Z",
     "iopub.status.busy": "2024-06-04T23:19:56.255003Z",
     "iopub.status.idle": "2024-06-04T23:19:56.284350Z",
     "shell.execute_reply": "2024-06-04T23:19:56.284045Z"
    }
   },
   "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>coef</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>posres</th>\n",
       "      <td>0.570773</td>\n",
       "      <td>0.175960</td>\n",
       "      <td>1.179610e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multi</th>\n",
       "      <td>-0.040860</td>\n",
       "      <td>0.251194</td>\n",
       "      <td>8.707842e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clinend</th>\n",
       "      <td>0.546183</td>\n",
       "      <td>0.262000</td>\n",
       "      <td>3.709944e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sampsize</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>7.507005e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>0.004386</td>\n",
       "      <td>0.002465</td>\n",
       "      <td>7.515984e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>impact</th>\n",
       "      <td>0.058318</td>\n",
       "      <td>0.006676</td>\n",
       "      <td>2.426306e-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               coef  se(coef)             p\n",
       "covariate                                  \n",
       "posres     0.570773  0.175960  1.179610e-03\n",
       "multi     -0.040860  0.251194  8.707842e-01\n",
       "clinend    0.546183  0.262000  3.709944e-02\n",
       "sampsize   0.000005  0.000015  7.507005e-01\n",
       "budget     0.004386  0.002465  7.515984e-02\n",
       "impact     0.058318  0.006676  2.426306e-18"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = MS(Publication.columns.drop('mech'),\n",
    "           intercept=False)\n",
    "coxph().fit(model.fit_transform(Publication),\n",
    "            'time',\n",
    "            'status').summary[['coef', 'se(coef)', 'p']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b2efa5",
   "metadata": {},
   "source": [
    "We see that there are a number of statistically significant variables,\n",
    "including whether the trial focused on a clinical endpoint, the impact\n",
    "of the study, and whether the study had positive or negative results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "838d91d3",
   "metadata": {},
   "source": [
    "## Call Center Data\n",
    "\n",
    "In this section, we will simulate survival data using the relationship\n",
    "between cumulative hazard and\n",
    "the survival function explored in Exercise \\ref{ex:all3}.\n",
    "Our simulated data will represent the observed\n",
    "wait times (in seconds) for 2,000 customers who have phoned a call\n",
    "center.  In this context, censoring occurs if a customer hangs up\n",
    "before his or her call is answered.\n",
    "\n",
    "There are three covariates: `Operators` (the number of call\n",
    "center operators available at the time of the call, which can range\n",
    "from $5$ to $15$), `Center` (either A, B, or C), and\n",
    "`Time` of day (Morning, Afternoon, or Evening). We generate data\n",
    "for these covariates so that all possibilities are equally likely: for\n",
    "instance, morning, afternoon and evening calls are equally likely, and\n",
    "any number of operators from $5$ to $15$ is equally likely. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "582c992b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.285647Z",
     "iopub.status.busy": "2024-06-04T23:19:56.285576Z",
     "iopub.status.idle": "2024-06-04T23:19:56.288228Z",
     "shell.execute_reply": "2024-06-04T23:19:56.287999Z"
    }
   },
   "outputs": [],
   "source": [
    "rng = np.random.default_rng(10)\n",
    "N = 2000\n",
    "Operators = rng.choice(np.arange(5, 16),\n",
    "                       N,\n",
    "                       replace=True)\n",
    "Center = rng.choice(['A', 'B', 'C'],\n",
    "                    N,\n",
    "                    replace=True)\n",
    "Time = rng.choice(['Morn.', 'After.', 'Even.'],\n",
    "                   N,\n",
    "                   replace=True)\n",
    "D = pd.DataFrame({'Operators': Operators,\n",
    "                  'Center': pd.Categorical(Center),\n",
    "                  'Time': pd.Categorical(Time)})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b823f59",
   "metadata": {},
   "source": [
    "We then build a model matrix (omitting the intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f3367dad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.289419Z",
     "iopub.status.busy": "2024-06-04T23:19:56.289350Z",
     "iopub.status.idle": "2024-06-04T23:19:56.295467Z",
     "shell.execute_reply": "2024-06-04T23:19:56.295239Z"
    }
   },
   "outputs": [],
   "source": [
    "model = MS(['Operators',\n",
    "            'Center',\n",
    "            'Time'],\n",
    "           intercept=False)\n",
    "X = model.fit_transform(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "229c9b6b",
   "metadata": {},
   "source": [
    "It is worthwhile to take a peek at the model matrix `X`, so\n",
    "that we can be sure that we understand how the variables have been coded. By default,\n",
    "the levels of categorical variables are sorted and, as usual, the first column of the one-hot encoding\n",
    "of the variable is dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fdfd2789",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.296977Z",
     "iopub.status.busy": "2024-06-04T23:19:56.296905Z",
     "iopub.status.idle": "2024-06-04T23:19:56.300297Z",
     "shell.execute_reply": "2024-06-04T23:19:56.300080Z"
    }
   },
   "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>Operators</th>\n",
       "      <th>Center[B]</th>\n",
       "      <th>Center[C]</th>\n",
       "      <th>Time[Even.]</th>\n",
       "      <th>Time[Morn.]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Operators  Center[B]  Center[C]  Time[Even.]  Time[Morn.]\n",
       "0         13        0.0        1.0          0.0          0.0\n",
       "1         15        0.0        0.0          1.0          0.0\n",
       "2          7        1.0        0.0          0.0          1.0\n",
       "3          7        0.0        1.0          0.0          1.0\n",
       "4         13        0.0        1.0          1.0          0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b1edac0",
   "metadata": {},
   "source": [
    "Next,  we specify the coefficients and the hazard function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1a9b68c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.301604Z",
     "iopub.status.busy": "2024-06-04T23:19:56.301530Z",
     "iopub.status.idle": "2024-06-04T23:19:56.305801Z",
     "shell.execute_reply": "2024-06-04T23:19:56.305262Z"
    }
   },
   "outputs": [],
   "source": [
    "true_beta = np.array([0.04, -0.3, 0, 0.2, -0.2])\n",
    "true_linpred = X.dot(true_beta)\n",
    "hazard = lambda t: 1e-5 * t\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e481dba7",
   "metadata": {},
   "source": [
    "Here, we have set the coefficient associated with `Operators` to\n",
    "equal $0.04$; in other words, each additional operator leads to a\n",
    "$e^{0.04}=1.041$-fold increase in the “risk” that the call will be\n",
    "answered, given the `Center` and `Time` covariates. This\n",
    "makes sense: the greater the number of operators at hand, the shorter\n",
    "the wait time! The coefficient associated with `Center == B` is\n",
    "$-0.3$, and `Center == A` is treated as the baseline. This means\n",
    "that the risk of a call being answered at Center B is 0.74 times the\n",
    "risk that it will be answered at Center A; in other words, the wait\n",
    "times are a bit longer at Center B.\n",
    "\n",
    "Recall from Section~\\ref{Ch2-statlearn-lab:loading-data} the use of `lambda`\n",
    "for creating short functions on the fly.\n",
    "We use the function\n",
    "`sim_time()` from the `ISLP.survival` package. This function\n",
    "uses the relationship between the survival function\n",
    "and cumulative hazard $S(t) = \\exp(-H(t))$ and the specific\n",
    "form of the cumulative hazard function in the Cox model\n",
    "to simulate data based on values of the linear predictor\n",
    "`true_linpred` and the cumulative hazard. \n",
    " We need to provide the cumulative hazard function, which we do here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d2118fdd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.310515Z",
     "iopub.status.busy": "2024-06-04T23:19:56.310277Z",
     "iopub.status.idle": "2024-06-04T23:19:56.313678Z",
     "shell.execute_reply": "2024-06-04T23:19:56.312612Z"
    },
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "cum_hazard = lambda t: 1e-5 * t**2 / 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9ceb02",
   "metadata": {},
   "source": [
    "We are now ready to generate data under the Cox proportional hazards\n",
    "model. We truncate the maximum time to 1000 seconds to keep\n",
    "simulated wait times reasonable. The function\n",
    "`sim_time()` takes a linear predictor,\n",
    "a cumulative hazard function and a\n",
    "random number generator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "68304ba6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.316852Z",
     "iopub.status.busy": "2024-06-04T23:19:56.316648Z",
     "iopub.status.idle": "2024-06-04T23:19:56.419919Z",
     "shell.execute_reply": "2024-06-04T23:19:56.419384Z"
    }
   },
   "outputs": [],
   "source": [
    "W = np.array([sim_time(l, cum_hazard, rng)\n",
    "              for l in true_linpred])\n",
    "D['Wait time'] = np.clip(W, 0, 1000)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "876110e5",
   "metadata": {},
   "source": [
    "We now simulate our censoring variable, for which we assume\n",
    "90% of calls were answered (`Failed==1`) before the\n",
    "customer hung up (`Failed==0`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "becf4cd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.423676Z",
     "iopub.status.busy": "2024-06-04T23:19:56.423359Z",
     "iopub.status.idle": "2024-06-04T23:19:56.434279Z",
     "shell.execute_reply": "2024-06-04T23:19:56.433259Z"
    },
    "lines_to_next_cell": 2
   },
   "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>Operators</th>\n",
       "      <th>Center</th>\n",
       "      <th>Time</th>\n",
       "      <th>Wait time</th>\n",
       "      <th>Failed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>After.</td>\n",
       "      <td>525.064979</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>A</td>\n",
       "      <td>Even.</td>\n",
       "      <td>254.677835</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>Morn.</td>\n",
       "      <td>487.739224</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>Morn.</td>\n",
       "      <td>308.580292</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>Even.</td>\n",
       "      <td>154.174608</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Operators Center    Time   Wait time  Failed\n",
       "0         13      C  After.  525.064979       1\n",
       "1         15      A   Even.  254.677835       1\n",
       "2          7      B   Morn.  487.739224       1\n",
       "3          7      C   Morn.  308.580292       1\n",
       "4         13      C   Even.  154.174608       1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D['Failed'] = rng.choice([1, 0],\n",
    "                         N,\n",
    "                         p=[0.9, 0.1])\n",
    "D[:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e480331c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.437399Z",
     "iopub.status.busy": "2024-06-04T23:19:56.437090Z",
     "iopub.status.idle": "2024-06-04T23:19:56.442236Z",
     "shell.execute_reply": "2024-06-04T23:19:56.441064Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9075"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D['Failed'].mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79caaaf0",
   "metadata": {},
   "source": [
    "We now plot  Kaplan-Meier survival curves. First, we stratify by `Center`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a194c96e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.445459Z",
     "iopub.status.busy": "2024-06-04T23:19:56.445197Z",
     "iopub.status.idle": "2024-06-04T23:19:56.618294Z",
     "shell.execute_reply": "2024-06-04T23:19:56.618033Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Probability of Still Being on Hold')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize=(8,8))\n",
    "by_center = {}\n",
    "for center, df in D.groupby('Center'):\n",
    "    by_center[center] = df\n",
    "    km_center = km.fit(df['Wait time'], df['Failed'])\n",
    "    km_center.plot(label='Center=%s' % center, ax=ax)\n",
    "ax.set_title(\"Probability of Still Being on Hold\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a794d828",
   "metadata": {},
   "source": [
    "Next, we stratify by `Time`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c9eae8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.619764Z",
     "iopub.status.busy": "2024-06-04T23:19:56.619647Z",
     "iopub.status.idle": "2024-06-04T23:19:56.725906Z",
     "shell.execute_reply": "2024-06-04T23:19:56.725634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Probability of Still Being on Hold')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize=(8,8))\n",
    "by_time = {}\n",
    "for time, df in D.groupby('Time'):\n",
    "    by_time[time] = df\n",
    "    km_time = km.fit(df['Wait time'], df['Failed'])\n",
    "    km_time.plot(label='Time=%s' % time, ax=ax)\n",
    "ax.set_title(\"Probability of Still Being on Hold\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf0f501",
   "metadata": {},
   "source": [
    "It seems that calls at Call Center B take longer to be answered than\n",
    "calls at Centers A and C. Similarly, it appears that wait times are\n",
    "longest in the morning and shortest in the evening hours. We can use a\n",
    "log-rank test to determine whether these differences are statistically\n",
    "significant using the function `multivariate_logrank_test()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bb8a37eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.727330Z",
     "iopub.status.busy": "2024-06-04T23:19:56.727224Z",
     "iopub.status.idle": "2024-06-04T23:19:56.739716Z",
     "shell.execute_reply": "2024-06-04T23:19:56.739485Z"
    },
    "lines_to_next_cell": 2
   },
   "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>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>multivariate_logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.30</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>14.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrr}\n",
       " & test_statistic & p & -log2(p) \\\\\n",
       "0 & 20.30 & 0.00 & 14.65 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.StatisticalResult: multivariate_logrank_test>\n",
       "               t_0 = -1\n",
       " null_distribution = chi squared\n",
       "degrees_of_freedom = 2\n",
       "         test_name = multivariate_logrank_test\n",
       "\n",
       "---\n",
       " test_statistic      p  -log2(p)\n",
       "          20.30 <0.005     14.65"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multivariate_logrank_test(D['Wait time'],\n",
    "                          D['Center'],\n",
    "                          D['Failed'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c798ea63",
   "metadata": {},
   "source": [
    "Next, we consider the  effect of `Time`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e3299d19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.741062Z",
     "iopub.status.busy": "2024-06-04T23:19:56.740984Z",
     "iopub.status.idle": "2024-06-04T23:19:56.753655Z",
     "shell.execute_reply": "2024-06-04T23:19:56.753435Z"
    },
    "lines_to_next_cell": 2
   },
   "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>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>multivariate_logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.90</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>35.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrr}\n",
       " & test_statistic & p & -log2(p) \\\\\n",
       "0 & 49.90 & 0.00 & 35.99 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.StatisticalResult: multivariate_logrank_test>\n",
       "               t_0 = -1\n",
       " null_distribution = chi squared\n",
       "degrees_of_freedom = 2\n",
       "         test_name = multivariate_logrank_test\n",
       "\n",
       "---\n",
       " test_statistic      p  -log2(p)\n",
       "          49.90 <0.005     35.99"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multivariate_logrank_test(D['Wait time'],\n",
    "                          D['Time'],\n",
    "                          D['Failed'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a3bb55e",
   "metadata": {},
   "source": [
    "As in the case of a categorical variable with 2 levels, these\n",
    "results are similar to the likelihood ratio test\n",
    "from the Cox proportional hazards model. First, we\n",
    "look at the results for  `Center`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b9004e25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.755032Z",
     "iopub.status.busy": "2024-06-04T23:19:56.754959Z",
     "iopub.status.idle": "2024-06-04T23:19:56.856726Z",
     "shell.execute_reply": "2024-06-04T23:19:56.856494Z"
    },
    "lines_to_next_cell": 2
   },
   "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>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_freedom</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>log-likelihood ratio test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.58</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>14.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrr}\n",
       " & test_statistic & p & -log2(p) \\\\\n",
       "0 & 20.58 & 0.00 & 14.85 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.StatisticalResult: log-likelihood ratio test>\n",
       "null_distribution = chi squared\n",
       "  degrees_freedom = 2\n",
       "        test_name = log-likelihood ratio test\n",
       "\n",
       "---\n",
       " test_statistic      p  -log2(p)\n",
       "          20.58 <0.005     14.85"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = MS(['Wait time',\n",
    "        'Failed',\n",
    "        'Center'],\n",
    "        intercept=False).fit_transform(D)\n",
    "F = coxph().fit(X, 'Wait time', 'Failed')\n",
    "F.log_likelihood_ratio_test()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbd4b435",
   "metadata": {},
   "source": [
    "Next, we look at the results for `Time`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1780384e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.858260Z",
     "iopub.status.busy": "2024-06-04T23:19:56.858163Z",
     "iopub.status.idle": "2024-06-04T23:19:56.951866Z",
     "shell.execute_reply": "2024-06-04T23:19:56.951640Z"
    },
    "lines_to_next_cell": 2
   },
   "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>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_freedom</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>log-likelihood ratio test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.12</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>34.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrr}\n",
       " & test_statistic & p & -log2(p) \\\\\n",
       "0 & 48.12 & 0.00 & 34.71 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.StatisticalResult: log-likelihood ratio test>\n",
       "null_distribution = chi squared\n",
       "  degrees_freedom = 2\n",
       "        test_name = log-likelihood ratio test\n",
       "\n",
       "---\n",
       " test_statistic      p  -log2(p)\n",
       "          48.12 <0.005     34.71"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = MS(['Wait time',\n",
    "        'Failed',\n",
    "        'Time'],\n",
    "       intercept=False).fit_transform(D)\n",
    "F = coxph().fit(X, 'Wait time', 'Failed')\n",
    "F.log_likelihood_ratio_test()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b02d23b",
   "metadata": {},
   "source": [
    "We find that differences between centers are highly significant, as\n",
    "are differences between times of day.\n",
    "\n",
    "Finally, we fit Cox's proportional hazards model to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "19f93f96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-04T23:19:56.953169Z",
     "iopub.status.busy": "2024-06-04T23:19:56.953096Z",
     "iopub.status.idle": "2024-06-04T23:19:57.089908Z",
     "shell.execute_reply": "2024-06-04T23:19:57.089269Z"
    },
    "lines_to_next_cell": 2
   },
   "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>coef</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Operators</th>\n",
       "      <td>0.043934</td>\n",
       "      <td>0.007520</td>\n",
       "      <td>5.143589e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Center[B]</th>\n",
       "      <td>-0.236060</td>\n",
       "      <td>0.058113</td>\n",
       "      <td>4.864162e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Center[C]</th>\n",
       "      <td>0.012231</td>\n",
       "      <td>0.057518</td>\n",
       "      <td>8.316096e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time[Even.]</th>\n",
       "      <td>0.268845</td>\n",
       "      <td>0.057797</td>\n",
       "      <td>3.294956e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time[Morn.]</th>\n",
       "      <td>-0.148217</td>\n",
       "      <td>0.057334</td>\n",
       "      <td>9.733557e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 coef  se(coef)             p\n",
       "covariate                                    \n",
       "Operators    0.043934  0.007520  5.143589e-09\n",
       "Center[B]   -0.236060  0.058113  4.864162e-05\n",
       "Center[C]    0.012231  0.057518  8.316096e-01\n",
       "Time[Even.]  0.268845  0.057797  3.294956e-06\n",
       "Time[Morn.] -0.148217  0.057334  9.733557e-03"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = MS(D.columns,\n",
    "       intercept=False).fit_transform(D)\n",
    "fit_queuing = coxph().fit(\n",
    "                  X,\n",
    "                 'Wait time',\n",
    "                 'Failed')\n",
    "fit_queuing.summary[['coef', 'se(coef)', 'p']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5870174",
   "metadata": {},
   "source": [
    "The $p$-values for Center B and evening time\n",
    "are very small. It is also clear that the\n",
    "hazard --- that is, the instantaneous risk that a call will be\n",
    "answered --- increases with the number of operators. Since we\n",
    "generated the data ourselves, we know that the true coefficients for\n",
    " `Operators`, `Center = B`, `Center = C`, \n",
    "`Time = Even.` and `Time = Morn.`   are $0.04$, $-0.3$,\n",
    "$0$,   $0.2$, and $-0.2$, respectively. The coefficient estimates\n",
    "from the fitted Cox model are fairly accurate.\n",
    "\n"
   ]
  }
 ],
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