{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-05T14:18:13.906771",
     "start_time": "2016-08-05T14:18:13.845923"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from numpy.random import uniform, exponential\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "from lifelines.utils import survival_table_from_events\n",
    "from lifelines.plotting import plot_lifetimes\n",
    "\n",
    "## plot/figure defaults\n",
    "plt.style.use('ggplot')\n",
    "matplotlib.rcParams['figure.figsize'] = (15.0, 8.0)\n",
    "plt.rcParams['font.size'] = 20\n",
    "plt.rcParams['axes.labelsize'] = 13\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['axes.titlesize'] = 13\n",
    "plt.rcParams['xtick.labelsize'] = 13\n",
    "plt.rcParams['ytick.labelsize'] = 13\n",
    "plt.rcParams['legend.fontsize'] = 13\n",
    "plt.rcParams['figure.titlesize'] = 'large'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-26T23:53:48.523607",
     "start_time": "2016-07-26T23:53:48.518882"
    },
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Survival Modeling in Stan\n",
    "\n",
    " -- TODO insert image here \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# a very brief introduction\n",
    "\n",
    "Survival analysis typically involves **time to event** as the outcome of interest\n",
    "\n",
    "Typical applications include:\n",
    "* biomedical research (time to clinical event or death)\n",
    "* digital marketing (time to click or KPI)\n",
    "* machine (time to mechanical failure)\n",
    "* psychology (e.g. studies of delayed gratification)\n",
    "* ... etc\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## key terms\n",
    "* censoring: when an event isn't observed, but we know \n",
    "   - it happened after time `b` (right censoring)\n",
    "   - it happened before time `a` (left censoring)\n",
    "   - it happened between times `a` and `b` (interval censoring)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Survival analysis typically makes an assumption of *non-informative censoring*. \n",
    "\n",
    "If this assumption holds, it means that the true event time (if it were to be observed) is unrelated to the censored time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In a context of a well-designed clinical trial, this assumption is addressed by the study design which imposes an independent censoring process. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For example, it's not uncommon for follow-up time to be truncated after a predetermined time $t=10$ for all patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:33.560853",
     "start_time": "2016-07-29T17:42:33.540195"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# (prep for example - borrowed from http://lifelines.readthedocs.io/en/latest/Survival%20Analysis%20intro.html)\n",
    "from numpy.random import uniform, exponential\n",
    "import numpy as np\n",
    "\n",
    "## example of censored data\n",
    "from lifelines.plotting import plot_lifetimes\n",
    "def plot_example():\n",
    "    N = 25\n",
    "    current_time = 10\n",
    "\n",
    "    actual_lifetimes = np.array([[exponential(12), exponential(5)][uniform()<0.3] for i in range(N)])\n",
    "    observed_lifetimes = np.minimum(actual_lifetimes, current_time)\n",
    "    observed = actual_lifetimes < current_time\n",
    "    fig = plt.figure()\n",
    "    _ = plt.xlim(0, 25)\n",
    "    _ = plt.vlines(10, 0, 30, lw=2, linestyles=\"--\")\n",
    "    _ = plt.xlabel('time')\n",
    "    _ = plt.title('Births and deaths of a hypothetical population, censored at $t=10$', size=20)    \n",
    "    _ = plot_lifetimes(lifetimes=observed_lifetimes, event_observed=observed)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:34.166750",
     "start_time": "2016-07-29T17:42:33.562899"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3MAAAIICAYAAADE27fMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b9c69aa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_example()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "*however*, this assumption can sometimes be violated if, for example, less healthy patients are more likely to drop out of the study early."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## key terms (cont'd)\n",
    "\n",
    "* failure event: the outcome event of interest\n",
    "* survival: not failure\n",
    "* hazard: risk for failure events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Survival**\n",
    "\n",
    "We first define a *Survival* function $S$ as the probability of surviving to time $t$:\n",
    "\n",
    "  $$ S(t)=Pr(Y > t) $$\n",
    "\n",
    "where $T$ is the true survival time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**hazard**\n",
    "\n",
    "We also define the instantaneous *hazard* function $\\lambda$ as the probability of a failure event occuring in the interval [$t$, $t+\\delta t$], given survival to time $t$:\n",
    "\n",
    "\n",
    "  $$ \\lambda(t) =  \\lim_{\\delta t \\rightarrow 0 } \\; \\frac{Pr( t \\le Y \\le t + \\delta t | Y > t)}{\\delta t} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Which is equal to \n",
    "\n",
    "  $$ \\lambda(t) = \\frac{-S'(t)}{S(t)} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Solving this \n",
    "\n",
    "  $$ \\lambda(t) = \\frac{-S'(t)}{S(t)} $$\n",
    "  \n",
    "yields the following:\n",
    "\n",
    "  $$ S(t) = \\exp\\left( -\\int_0^t \\lambda(z) dz \\right) $$\n",
    "\n",
    "which provides a useful way for us to switch from modeling *hazards* to modeling *Survival*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The integral in this equation is also sometimes called the *cumulative hazard*, here noted as $H(t)$.\n",
    "\n",
    "  $$ H(t) = \\int_0^t \\lambda(z) dz $$\n",
    "\n",
    "It's worth pointing out that, by definition, the cumulative hazard (estimating $Pr(Y \\lt t)$) is the complementary c.d.f of the Survival function (which estimates $Pr(Y \\ge t)$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "Let's consider a simple *hazard* function $\\lambda(t)$ as constant over time. \n",
    "\n",
    "  $$ \\lambda(t) = a $$\n",
    "\n",
    "Cumulative hazard ($H$) would be: \n",
    "\n",
    "  $$ H(t) = \\int_0^t \\lambda(z) dz = at $$\n",
    "\n",
    "And the *Survival* function would be:\n",
    "\n",
    "  $$ S(t) = \\exp\\left( -\\int_0^t \\lambda(z) dz \\right) = \\exp ( − a t ) $$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Graphically, this would look like the following: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:34.192136",
     "start_time": "2016-07-29T17:42:34.168280"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "## prep example plot for hazard <> Survival curves\n",
    "def plot_survival_exp(N, censor_time, rate):\n",
    "\n",
    "    sample_data = pd.DataFrame({\n",
    "            't': np.linspace(0, censor_time, num=N),\n",
    "            })\n",
    "    sample_data['hazard'] = rate\n",
    "    sample_data['cum_hazard'] = rate*sample_data.t\n",
    "    sample_data['Survival'] = np.exp(-1 * rate * sample_data.t)\n",
    "    fig = plt.figure()\n",
    "    _ = plt.subplot(131)\n",
    "    _ = plt.plot(sample_data.t, sample_data.hazard, 'b')\n",
    "    _ = plt.title('Hazard $\\lambda(t)$', size=20)\n",
    "    _ = plt.xlabel('time')\n",
    "    _ = plt.subplot(132)\n",
    "    _ = plt.plot(sample_data.t, sample_data.cum_hazard, 'r')\n",
    "    _ = plt.title('Cum. hazard $H(t)$', size=20)\n",
    "    _ = plt.xlabel('time')\n",
    "    _ = plt.subplot(133)\n",
    "    _ = plt.plot(sample_data.t, sample_data.Survival, 'g')\n",
    "    _ = plt.title('Survival $S(t)$', size=20)\n",
    "    _ = plt.xlabel('time')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:34.819847",
     "start_time": "2016-07-29T17:42:34.193691"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA38AAAIJCAYAAAAVu4TnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b5db19c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_survival_exp(N = 1000, censor_time = 10, rate = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-05T14:19:10.917055",
     "start_time": "2016-08-05T14:19:10.888990"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "## prep simulate-data example under Exponential model\n",
    "def plot_cum_survival(t, event):\n",
    "    # at each time t, calculate the cumulative survival\n",
    "    cum_survival = survival_table_from_events(t, event)\n",
    "    cum_survival.reset_index(0, inplace=True)\n",
    "    cum_survival.rename(columns = {'event_at': 't'}, inplace=True)\n",
    "    cum_survival['Survival'] = cum_survival['at_risk']/max(cum_survival['at_risk'])\n",
    "    # create figure\n",
    "    fig = plt.figure()\n",
    "    _ = plt.plot(cum_survival.t, cum_survival.Survival, 'rs')\n",
    "    _ = plt.xlabel('time')\n",
    "    return(cum_survival)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can also simulate survival times for a population under this fairly simple model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:34.855137",
     "start_time": "2016-07-29T17:42:34.840401"
    },
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# define a function to simulate data\n",
    "def simulate_exp_survival_data(N, censor_time, rate):\n",
    "    ## simulate true lifetimes (t) according to exponential model\n",
    "    sample_data = pd.DataFrame({\n",
    "            'true_t': np.random.exponential((1/rate), size=N) \n",
    "            })\n",
    "    ## censor observations at censor_time\n",
    "    sample_data['t'] = np.minimum(sample_data['true_t'], censor_time)\n",
    "    sample_data['event'] = sample_data['t'] >= sample_data['true_t']\n",
    "    return(sample_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:34.882173",
     "start_time": "2016-07-29T17:42:34.856839"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>true_t</th>\n",
       "      <th>t</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.137413</td>\n",
       "      <td>0.137413</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.310870</td>\n",
       "      <td>4.310870</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.685407</td>\n",
       "      <td>1.685407</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.228120</td>\n",
       "      <td>2.228120</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.632862</td>\n",
       "      <td>0.632862</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     true_t         t event\n",
       "0  0.137413  0.137413  True\n",
       "1  4.310870  4.310870  True\n",
       "2  1.685407  1.685407  True\n",
       "3  2.228120  2.228120  True\n",
       "4  0.632862  0.632862  True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simulate data assuming constant hazard over time of 0.5\n",
    "df = simulate_exp_survival_data(N = 100, censor_time = 6, rate = 0.5)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:35.628977",
     "start_time": "2016-07-29T17:42:34.883727"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3cAAAHnCAYAAAAbyqR8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b5dda4bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot lifelines for this simulated sample\n",
    "plot_lifetimes(event_observed=df.event, lifetimes=df.t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:35.905871",
     "start_time": "2016-07-29T17:42:35.630534"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3MAAAIGCAYAAAD+0da8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b5df0ba10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate number of people surviving at each time T\n",
    "# plot Survival to time t given simulated data\n",
    "a = plot_cum_survival(df.t, df.event)\n",
    "_ = plt.title('Simulated survival with $\\lambda={}$'.format(0.5), size=20)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now, overlay computed Survival from simulated data with estimated c.d.f "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:36.171941",
     "start_time": "2016-07-29T17:42:35.907409"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3cAAAH5CAYAAAAiFseXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b5dd71350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = plot_cum_survival(df.t, df.event)\n",
    "## overlay c.d.f. estimate as exp(-at)\n",
    "\n",
    "test = pd.DataFrame([{\n",
    "            't': t,\n",
    "            'Estimated Surv': np.exp(-1 * 0.5 * t)\n",
    "        } for t in np.linspace(0, 10, num=100)])\n",
    "_ = plt.plot(test.t, test['Estimated Surv'], 'b')\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# the likelihood\n",
    "\n",
    "The data for survival analysis typically constitute a set of observed pairs: [`t`, `status`] for each subject, where \n",
    "\n",
    "* `t` is the survival time (last time a subject was observed alive)\n",
    "* `status` is a binary (T/F or 1/0) indicator for whether the failure event occurred at time `t`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "\n",
    "The likelihood for an observation $i$ with time $t_i$ and `status = 1` (DECEASED) will reflect the joint probability of surviving to time $t$ and having an event at time $t$:\n",
    "\n",
    "   $$ L_i = f(t_i) = S(t_i)\\lambda(t_i) $$ \n",
    "\n",
    "For an observation that is censored at time $t$, we only have a contribution to the Survival function :\n",
    "\n",
    "   $$ L_i = f(t_i) = S(t_i) $$ \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Most of the time, we are interested in modeling the impact of covariates on these outcomes. \n",
    "\n",
    "The typical starting point is to make what's called the *Proportional Hazards* assumption. \n",
    "\n",
    "IE: \n",
    "\n",
    "  $$ h_X(t_i) = e^{\\beta X}h(t_i) $$\n",
    "  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-28T02:38:37.689913",
     "start_time": "2016-07-28T02:38:37.668224"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For example, in the context of our previous example of Survival times simulated according to the Exponential model, we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:36.197095",
     "start_time": "2016-07-29T17:42:36.173545"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "## prep simulate-data example under Exponential model\n",
    "def plot_cum_survival_X(df):\n",
    "    # at each time t, calculate the cumulative survival\n",
    "    data_true = df.loc[df['X'] == True,]\n",
    "    cumsurv_true = survival_table_from_events(data_true.t, data_true.event)\n",
    "    cumsurv_true.reset_index(0, inplace=True)\n",
    "    cumsurv_true['X'] = True\n",
    "    cumsurv_true.rename(columns = {'event_at': 't'}, inplace=True)\n",
    "    cumsurv_true['Survival'] = cumsurv_true['at_risk']/max(cumsurv_true['at_risk'])\n",
    "\n",
    "    data_false = df.loc[df['X'] == False,]\n",
    "    cumsurv_false = survival_table_from_events(data_false.t, data_false.event)\n",
    "    cumsurv_false.reset_index(0, inplace=True)\n",
    "    cumsurv_false['X'] = False\n",
    "    cumsurv_false.rename(columns = {'event_at': 't'}, inplace=True)\n",
    "    cumsurv_false['Survival'] = cumsurv_false['at_risk']/max(cumsurv_false['at_risk'])\n",
    "    \n",
    "    cum_survival = cumsurv_true.append(cumsurv_false)\n",
    "    # create figure\n",
    "    fig = plt.figure()\n",
    "    _ = sb.lmplot(data = cum_survival, x='t', y='Survival', hue='X', fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:36.216364",
     "start_time": "2016-07-29T17:42:36.198620"
    },
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# define a function to simulate data\n",
    "def simulate_exp_survival_data_X(N, censor_time, rate, beta):\n",
    "    ## simulate true lifetimes (t) according to exponential model\n",
    "    sample_data = pd.DataFrame({\n",
    "            'X' : [np.random.uniform()>0.5 for n in np.zeros(N)],\n",
    "            'baseline_hazard': np.repeat(rate, repeats=N),\n",
    "            })\n",
    "    sample_data['hazard'] = np.exp(beta*sample_data['X'])*sample_data['baseline_hazard']\n",
    "    sample_data['true_t'] = sample_data.apply(lambda row: np.random.exponential(scale = 1/row['hazard']), axis = 1)\n",
    "    ## censor observations at censor_time\n",
    "    sample_data['t'] = np.minimum(sample_data['true_t'], censor_time)\n",
    "    sample_data['event'] = sample_data['t'] >= sample_data['true_t']\n",
    "    return(sample_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:36.556127",
     "start_time": "2016-07-29T17:42:36.217994"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b5dd7d410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAFMCAYAAAAZVsFSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtcVHX+P/DXGWBghuEaiI2X4aJoZIJp3mITwTRrv0Wm\nkUV2022rtcc+vm3b0tpudtH2V+7mrqmp1ZLx1a22dC2zBJViyWwt84JuKjCIiDoh1xmYGeb8/hhn\n5Cooc+acwdfz8diHnDNn/LzBlvd8zvvzeR9BFEURREREElDJHQAREfVfTDJERCQZJhkiIpIMkwwR\nEUmGSYaIiCTDJENERJJhkiEiIsn4yx1Ad4qLi/H555+jvLwcVqsVGzZs6Pba77//Hlu2bIHRaIQo\nihgyZAjmzp2LkSNHejFiIiLqSLFJRqfTYcaMGWhpacGaNWsuem1TUxNmzpyJa6+9FkFBQcjPz8eS\nJUvw+uuvIzIy0ksRExFRR4q9XTZ69GhMnjwZMTExPV6bmpqKG264AVqtFiqVCtOnT0dQUBCOHTvm\nhUiJiKg7ik0yfVFRUYGGhgYMHTpU7lCIiK5o/S7J1NXVYdmyZbj99tsxcOBAucMhIrqiKbYmczlq\namrw8ssvIyUlBXPnzpU7HCKiK16/STJnzpzBiy++iAkTJiA7O1vucIiICApOMg6HA62trbDZbADg\n/jMgIKDTtSdPnsRLL72EtLQ0ZGVlXfJYVVVVfQu2D/R6vazjKyEGucdXQgxyj6+EGOQe3xUDeZZi\nk8yXX36JVatWuY9ds5M33ngDR44cwdq1a5GbmwsA2Lx5M2pqarB161Z8+umnAABBELBgwQKkpqZ6\nP3giIgIACHxoGWcycscg9/hKiEHu8ZUQg9zju2Igz+p3q8uIiEg5mGSIiEgyTDJERCQZJhkiIpIM\nkwwREUmGSYaIiCTDJENERJJhkiEiIskwyRARkWSYZIiISDJMMkREJBkmGSIikgyTDBERSYZJhoiI\nJMMkQ0REkmGSISIiyTDJEBGRZJhkiIhIMkwyREQkGSYZIiKSDJMMERFJhkmGiIgkwyRDRESSYZIh\nIiLJMMkQEZFkmGSIiEgyTDJERCQZJhkiIpIMkwwREUnGX+4AulNcXIzPP/8c5eXlsFqt2LBhw0Wv\n37dvH9avX4/Tp09j4MCBmDdvHkaPHu2laImIqCuKncnodDrMmDEDDz74YI/XnjlzBsuWLcOdd96J\n3NxcZGZm4tVXX4XJZJI+UCIi6pZik8zo0aMxefJkxMTE9Hjtrl27EB8fj9TUVPj5+SE1NRXx8fHY\ntWuX9IESEVG3FJtkLoXRaER8fHy7c3FxcTAajTJFREREgIJrMpeiubkZWq223TmtVovKykqZIuqZ\nzW7HG5/sg7HOBoO1Bo8nBiDgZ9MgqPpF3iciAtBPZjJBQUEwm83tzpnNZmg0Gpki6tkbn+xDcX0g\nTgrBKA4cgpX76yAWF8gdFhGRR/WLmYzBYEBJSUm7c2VlZbjuuut69X69Xi9FWBdVYVEBEN3H5UHR\nCK49hQgZYgHk+RkoaXwlxCD3+EqIQe7xyfMUm2QcDgdaW1ths9kAwP1nQEBAp2unTJmCLVu2oLi4\nGOPHj8fXX3+NsrIyLFy4sFdjVVVVeS7wXhqqceCETYBDECBCQKDDjoawKFhkiEWv18vyM1DK+EqI\nQe7xlRCD3OO7YiDPUuztsi+//BLZ2dlYsmQJHA4HsrOzkZ2dDZPJhKKiIjzwwAPua2NiYvCb3/wG\n//znP/HQQw9h06ZNePrppxEVFSXjd3BxT/w8BfGaVqgEQCvaYNWGYMcJCxxF2yE6HHKHR0TkEYqd\nyaSlpSEtLa3L11JTU5GamtruXHJyMpYtW+aFyDwjwN8fw+OvhnDODuu5n4AGK4wNLRBLdwEAhNSb\n5Q2QiMgDFDuTuRLEhgc6v7C2AAAM1hrn8UkuvSai/oFJRkZpcaEYFh0MR0AghlprMKXxv84XBhnk\nDYyIyEOYZGS0q6wex842QRUSgoqrYlEYORrQ6gBRZF2GiPoFJhkZlde2nP9KACDAiGDA3AixcBv3\nzBBRv8AkI6PY8ECIooj6FjtMVqBJpYZ7/sK6DBH1A0wyMkqPD8PwATpYW0Wo/VQwqiOxUzfC+SLr\nMkTUDyh2CfOVQCUI0AUGIEobAMAf8HOgImgYhHHxECZnyB0eEVGfcSYjs4QorfN2WZMd9QFaDInQ\nACeNEIsLWPwnIp/HmYzMRAgQAGftv7kZYvkBiI3/BY46e7FxUyYR+TImGZmVmpoQEuiPEACoqcUJ\ndeSFF1n8JyIfx9tlMhsWHXzhQB14Ydc/wOI/Efk8JhmZ3XrtQMSGq+EQRQzVX4UpYc2Aw+FMMBOn\nyh0eEVGfMMnIbOuhapTXWqESBFRU/YTCuiBApXLeKtu9U+7wiIj6hElGZsfONl04sLbAyJoMEfUj\nTDIyGxYdfGHXv0rbftc/azJE5OOYZGT281FXIy4i0LnrPygQFVfFYWfCVAhpM7khk4h8Hpcwy0wl\nCNCq/c7v+geAUFQMGA9hTLRzQ+ZJIzDIAGFyBgQVPxMQkW9hklGA2PBAlJyxtDsWiwsg7vrMeYIb\nM4nIRzHJKEB6fBgAoOxcMyw2B8rPtSD/RDOmos39TC4CICIfxPsvCqASBExLCEdcRBDKa60oOWvB\nNn/DhY7MABcBEJFPYpJRkAsPMQOgC0FF/PXAsGuAQQaIleVwFG1n00wi8im8XaYgseGBOHTajAZr\nK6x2EeZBgyDaayEUOmsz4rHDAFibISLfwZmMgqTHh7mXMwf6CyivbcGOyub2F7E2Q0Q+hDMZBem8\nnBmoCB7Y/iLWZojIhzDJKEzH5cyGa4ZBiJzpnMGc3y9DROQreLtMYdLiQt1dmWPD1UiLC5E7JCKi\ny8aZjMLsKqt3d2Uur7ViZ/4eZPyHmzKJyDdxJqMw7ZYxA6iosbS/gIV/IvIhTDIKYwhTOzsyN9lQ\n32LHkAhN+wtY+CciH6LY22UOhwN5eXkoLCyEzWZDcnIyFixYgJCQrmsUn3/+ObZu3Yra2lqEh4fj\ntttuw/Tp070cdd+JECAAgAAIAIRhIyGEOAv/on4oIIpw/GMdm2YSkU9Q7G+oTZs2Ye/evVi6dClW\nr14NURSxYsWKLq/dv38/3nvvPSxcuBC5ubl44oknsH79ehw4cMDLUfddRV0LQgL9EaUNQEigPyrq\nbVCl3gxV1nwIggCxcBvEoyUQd30GsbhA7nCJiC5KsUmmoKAAmZmZiI6OhkajQXZ2Nvbt2weTydTp\nWqPRiNjYWAwbNgwAkJiYCIPBAKPR9+oXseGB3R93rMewPkNECqfIJGM2m2EymRAXF+c+FxMTA41G\n02XiSElJQVVVFf773/9CFEUcPnwYp06dQkpKijfD9oj0+DDMHB4GnVqFELUKDhFwiKLzxY71GNZn\niEjhFFmTsVicK6q0Wm2788HBwe7X2hoyZAjmzJmD559/3n3uwQcfxODBgyWNUwoqQYAgCGi0Ohth\nbjtaC5UATEsIByZOdS5jPlEGDIlzHhMRKZgik4xG41xRZTab251vampyv9bW9u3bsW3bNixbtgx6\nvR6VlZX405/+BLVajalTfe8XccdlzO7j3Tudt8hUKuefu3cC3DNDRAqmyCSj1WoRFRWFsrIyGAzO\nW0LV1dWwWCzu47b27t2L8ePHQ6/XAwAGDx6MG264AXv37u1VknG9Ty4dx082ifhPlREt9lYE+vsh\n2TAAer0e52pNaAm40NcssNaECA/FrrSfwZUYg9zjKyEGuccnz1NkkgGAjIwMbN68GUlJSdDpdMjL\ny0NKSgqioqI6XRsXF4fdu3cjPT0dAwcORGVlJb799ttez2Kqqqo8HX6v6fX6TuOfq61Da6sdrQ4R\nra0iztXWoapKgCM8CqLN5r7OHh4Fiwdi7yoGb5J7fCXEIPf4SohB7vFdMZBnKTbJZGZmwmw2Iycn\nB3a7HcnJyVi4cCEAoKioCGvXrkVubi4AYNasWbBYLFi8eDHMZjN0Oh0mTZqEzMxMOb+Fy+ZaxhzS\n5hjAheaYbJZJRD5CEEXX0qUrl9I+veUfr8XWH2vdx7cmhjsL/wBEh8O5P6ZNounrhky5P0HKPb4S\nYpB7fCXEIPf4rhjIsxQ7k7mSpceHAXAW/A1hajhEYN3e04gND0Ra1bfuJ2WyYSYRKR2TjAKpBME9\nc8k/XovPzs9qSs5Y4KhtRrubZNyQSUQKpsjNmHRBp67MfFImEfkQJhmFiw0PhCiK7s7MTVGD4Ljp\nFkCrc/5PFCE6HHKHSUTUJSYZhUuPD0NcRCCsrSIC/QUY61qw06wDzI2AudHZMJONMolIoViTUTiV\nIECr9kOU9sImTD7IjIh8BWcyPqBjZ+ahkXyQGRH5Bs5kfEBaXChKzphReq4F8RGBmHrDeAi6Jm7K\nJCLF40zGB+wqq0d5rRUqQUB5rRWFxkZnYhlkcD4xs7iAxX8iUiTOZHxAV12ZxeICiLu4KZOIlI0z\nGR/QcRmz2doKRyWfkklEysck4wM6LmMur23BjvCk9hex+E9ECsQk4wPaLmMOCfQHIKAiKh5C2kxg\n2DXAIAPEynI4irazNkNEisIk4yM6LmOOjQiCKvVmCINjnbfKjh2GuOszbswkIkVhkvERaXGhiA1X\nwyGKiA1XIy0u1PlCx1oMazNEpCBMMj6i4zLmXWX1zhc61mJYmyEiBeESZh/R1TJmgE/LJCJl40zG\nRxjC1O4lzPUtdhjC1AAAQaXixkwiUizOZHyECAECAAiAcP7Y/Ro3ZhKRQjHJ+IiKuhaEBPojpM2x\nG4v/RKRQvF3mIzotYW57zOI/ESkUZzI+Ij0+DKIo4itjAwQADhFwiCJUggBhcgZEUQS+LXJefP5p\nmYKKnyGISF5MMj5CJQgQBAGNVmdRf9vRWqgEYFpCuLP4LwgQzY0AALFwGyAIrMsQkez4UdeHdLeM\nGQDrMkSkSEwyPqS7ZcwAWJchIkXi7TIfcrFlzNyUSURKxJmMD3EtY3Z1Y267jJmbMolIiS46k1m8\neHGPf4EgCPjDH/7gsYCoe7HhgSg5Y2l33BY3ZRKR0lw0yZSUlHgrDuqF9PgwAEDZuWZYbA6Un2tB\n/vFapMeHQSUILP4TkeJcNMnMnj3bW3F04nA4kJeXh8LCQthsNiQnJ2PBggUICQnp8vr6+nqsX78e\n3333Hex2OwYOHIicnByEh4d7OXLpqAQB0xLCkX+8Flt/rAUAlJx1zmymJYQ7b5UdbfPBgMV/IpLZ\nRZPMnDlzvBVHJ5s2bcLevXuxdOlS6HQ6rFy5EitWrEBOTk6na202G1544QUkJiZi+fLl0Ol0qKys\nRFBQkAyRS+9iHZm5KZOIlOSSVpc1Njbi+PHjqKurc/4yO2/KlCkeD6ygoABz5sxBdHQ0ACA7OxtP\nPvkkTCYToqKi2l27a9cumM1mzJ8/H6rzv1AHDx7s8ZiUwhCmxu4TDbDaRaj9hfYdmbkpk4gUpNdJ\n5vDhw3jttdfQ2NjY7rwgCB5PMmazGSaTCXFxce5zMTEx0Gg0MBqNnZLMoUOHcPXVV2PFihX44Ycf\nEBoaimnTpuG2227zaFxKcbGlzKzLEJGS9Po+ysaNGzslGADtZjSeYrE46wxarbbd+eDgYPdrbTU0\nNODgwYNITEzEmjVrsHDhQnz00UcoKiryeGxKcLGlzNyUSURK0uskU15ejpEjR2LGjBkAgDfffBOD\nBw/G008/7fGgNBoNAOeMpq2mpib3ax2vj4yMxC233AI/Pz/Ex8fjZz/7Gf7zn/94PDYluGhH5olT\nnYnF4XD+OXGql6MjIrqg17fLrFYrDAYD1Grn/X+tVovhw4dj/fr1GDdunEeD0mq1iIqKQllZGQwG\n5yfx6upqWCwW93FbsbGxKC0tvezx9Hr9Zb/XEy51/Oyrr0ZYWBW+OHIGAoDQsHAMvPpqqAQBjV9s\nRuOZKiAwEDhTBd2R76GbfofHY/A0ucdXQgxyj6+EGOQenzyv10kmODgYzc3N7kL8ypUrUVJS0uXt\nK0/IyMjA5s2bkZSUBJ1Oh7y8PKSkpHSqxwBAWloaNm/ejC+++ALTpk1DRUUFioqKMH/+/F6NVVVV\n5enwe02v11/W+HV1dfipwfmz3/htOerrajEtIRyOQz9AtNnc19Ue+gH1o26QJAZPkXt8JcQg9/hK\niEHu8V0xkGf1Osno9XqcPHkSM2fOBAB8/fXXAICkpCRJAsvMzITZbEZOTg7sdjuSk5OxcOFCAEBR\nURHWrl2L3NxcAEBUVBRycnKQm5uL9957DxEREbj77rsxceJESWJTgm47MnOvDBEpiCD2snJ/6tQp\nNDQ0IDExEVu3bkVhYSEiIiLw0EMPISYmRuo4JeWLn962HzuHfxz8yb2MOWvUVbh5WAREhwNicUG7\nRpk97ZOR+xOk3OMrIQa5x1dCDHKP74qBPKvXMxmLxYLExEQAwK233opbb71VsqCoZxddxkxEpBC9\nTjI5OTmIj49HRkYGUlNT++1uel/hWsYc0uYYYJNMIlKWXicZlUqF0tJSlJaWYv369bjxxhsxbdo0\nxMfHSxkfdaPbjszcjElECtLrJLNmzRr8+9//xpdffonjx4+joKAABQUFiIuLwyuvvCJljNSF7joy\np+kNEFj4JyKF6HWSCQkJwS233IJbbrkFVVVVeOutt3Dw4EGUlZVJGR91o7uOzI6EscgYVAKcKAOG\nxHEzJhHJ6pIaZJ4+fRpfffUVvvzyS5w+fRoA3A0pSR4dlzJXHDnuvEWmUjn/3L0TYE2GiGTS6yTz\n3HPP4ccff3QfR0VFIT09Henp6ZIERr0TGx6IQ6fNaLC2wmoX0Wg1w4E2/YJYkyEiGfV6GvLjjz9C\npVLhhhtuQE5ODlasWIG77roLERERUsZHPUiPD0NcRCCsrSIC/QVUBERgp27EhQtYkyHyWXV1dZgy\nZQoOHjzoPrd69Wo8+eSTMkZ1aXo9k5k7dy7S0tL61ZMm+wOVIECr9kOUNsB5IjAEFfHXQ7D4uTdj\nEpFvCgsLwx/+8Af87ne/w8cff4zS0lJs2LABmzZtkju0Xuv1TCYzM5MJRqHad2UWYLhmmHMGc9Lo\n3DfjcMgWGxH1TUZGBpKSkvDaa68hJycHzz77rE/dQbroTCYrKwu33XYb5s2bh6ysrC6vEQQBGzdu\nlCQ46p1Oy5kPH0N+aSmmNv4XKm7IJPJ5ixYtwtSpU3HjjTe6H7fiK3qcyfTU2kyKh5bRpXEtZ46L\nCEJ5rRWHG4BtoUkXajMs/hP5tOLiYoSEhKC0tBS2Nl3WfcFFZzJ//OMfERkZ6f6alM29nFkdCLQ0\nw6h2/tux+E/ku2pqarB06VKsXbsWb7/9Nv7617/iqaeekjusXrtokmnbxj8oKIgtZBTO1WpGDNah\noVXACdhREDka6Q4RgsPRYzdmIlKeF154AVlZWUhMTMSzzz6LzMxMzJgxA6NGjZI7tF7p9W+dnJwc\n5OTkID8/H83NzVLGRJcpPT4MtyaGIyTQH4CABvhjW1ACdnxX5mz/T0Q+5bPPPoPRaMSjjz4KAAgN\nDcVzzz2HZ599Fna7XeboeocNMvsRV22mvLYFjdUXmmca1ZGsyxD5oJkzZ7ofFOkydepUTJ3qO+2i\nej2TWbNmDR566CEkJCSgubkZBQUFyMnJwe9+9zsp46PLEBse6KzLnGew1rAuQ0SyYIPMfig9Pgyi\nYwiMh1swtKkaacPiURAzDsa9pxEbHoj0+DCoBD7kjIikxwaZ/ZBKEHDz8AiICWMhFhcg/0Qztp0+\nAehC3M+gmZbAjbVEJD02yOzHXE/JNEZOAoLqnCd1oZ06NxMRSaXXScbVIHPs2LGYNm0akpOTIfCW\ni7KdL/YbrDU4HDQQorUFDS12nKh1PuCMt82ISGqX1CBz6tSpCAsLkzIe8qRBBuBoCaY2/hcAUDRk\nPBoBNFhb3Q86420zIpJSrwoqdrsdGzZswFtvvSV1PORJE6cCgwxQORzICGvG0KED3XtogM4PPCMi\n8rReJRl/f39ERUUhKChI6njIk3bvbPeUzKE/tV8J2L57MxH1d6+//jrmz5/v1TF7vTRszpw52LNn\nD3744Qef2Wl6xeuwATPtXAliw9VwiCJiw9VIiwuVKTAi6ov7778f1113Ha6//nqMGTMG119/PZ57\n7rlevdfbtfRe12RWrVoFAFiyZEm782z1r2DnazIuuyKSUF5rhUoQUF5rxa6yetZkiHzUE088gV/+\n8pdyh9GjPm9yYat/5RImZwBTbgG0OkCrQ4XVH8CFfy/WZIj6l8OHDyM7OxsTJkzAhAkT8Oijj6Ky\nsrLb6//+978jPT0dY8eOxZQpU/DXv/7V/drJkyexcOFCpKam4qabbsLzzz9/WX0rez2TYat/3yOo\nVBAEAaK5EQAwpPR77B54I6z+gVD7CzCEqWWOkKj/sB4tQdOubVAFBkF32xz4XRXt9RgEQcCvf/1r\njBkzBhaLBc8++yyeeeYZ5OXldbr2+PHjWL58OT766CPExcWhoaHB3cGlubkZDzzwAGbNmoVly5ah\nubkZTz31FJYsWYIXXnjhkmLqdZJp2/affEibuowIAK12ICAQAgAR3CND5Al202mcW/0aRLvzgWK2\nilJc9ez/k/TxGqtWrcLbb78NURQhCALWrVuH0aNHu1/X6XR47LHHMHv2bNhsNgQEBLR7v7+/89f/\n0aNHERMTg5CQEPf7d+zYAX9/fzz++OMAALVajV/96leYN2+edEnmww8/7Pa12bNnX9KgveFwOJCX\nl4fCwkLYbDYkJydjwYIFCAkJuej7vvjiC7z11lvIysrCrFmzPB6Xz2lTlzmhjkSoWgVonf+xVdTx\ndhmRJ9hPnnAnGACwnz0NsakRQoh0i2see+yxTjUZo9GIV199Ffv374fZbIYoinA4HDh37hwGDBjQ\n7lqDwYBXXnkFGzZsQE5ODq655ho88cQTmDRpEiorK1FZWYnx48e7r3c4HBAEATU1Ne6HWfZGr5PM\nBx980O1rUiSZTZs2Ye/evVi6dCl0Oh1WrlyJFStWICcnp9v3mEwmfPLJJxg6dKjH4/FVwuQM5xcn\njTCEDcJh8UKS5hJmIs8IGGyAoFZDtFoBAP4DBkII1nk9jueeew5DhgzBp59+ipCQEBw5cgR33nln\nt7XzGTNmYMaMGbDb7Xjvvffw+OOP45tvvoFer8ewYcOwadOmPsfU67ncNddcg6SkJCQlJWHEiBHu\nGUViYmKfg+hKQUEBMjMzER0dDY1Gg+zsbOzbtw8mk6nb96xatQpz586FTuf9f1ylElQqqFJvhjDn\nYaQHN+GW2oO4xnoWhjA1ys8528s4uHiDqE/8ropGxC9/i6DkG6CZ8DNEPP47WZ5E29jYCK1Wi+Dg\nYNTU1LQr5Hd0/Phx/Pvf/0ZzczP8/f0RHBwMQRCgUqmQnp4Os9mMtWvXwmw2AwCqq6uRn59/yTH1\neibz/PPPtzu22Wx46aWXkJCQcMmD9sRsNsNkMiEuLs59LiYmBhqNBkajEVFRUZ3es337dgQFBWHS\npEn44osvPB6TrxOLCyAUfoYMAAW6EdhmDQR0oSg5a0F4+CmMu0ruCIl8mzphBNQJI7wyVnd7XRYt\nWoQ//vGPGDt2LAYPHowHHngAO3fu7PJam82Gv/3tbzh27BgEQYDBYMAbb7wBf39/+Pv7491338Wr\nr76KW265BWazGTExMfj5z3+OadOmXVKsl9Tqv62AgAAkJCRg9+7dmDdv3uX+NV2yWJzt6LVabbvz\nwcHB7tfaMplM+Pjjjzvt4aE22iwAMKojAeuFesyxs00Yd1WwHFER0WV49913uzx//fXXY8uWLe3O\ntS1n/PrXv3Z/PXLkyIvucRw4cCCWLVvWx0gvIcmsXLmy3XFjYyN++OEHSVrNaDQaAHBP01yamprc\nr7X15ptvYtasWQgP58bCbrVZAGCw1uCwegREUUSDtRWlpkbkH7exKzMReVyvk0xhYWGX5ydOnOix\nYFy0Wi2ioqJQVlYGg8H52ODq6mpYLBb3cVv79+9HaWkpNmzYAMCZnI4fP44ffvgBixcv7nE8vV7v\n2W/gEnljfMes+3DuZDlspT/itoEqhE8Zhe0/noXlnAV1zXbkl9kRHh6O26+T52ch97+BEmKQe3wl\nxCD3+OR5vU4yN910U7v7gEFBQUhISMDPfvYzSQLLyMjA5s2bkZSUBJ1Oh7y8PKSkpHRZj3G1vHH5\n85//jGuuuQb/8z//06uxqqqqPBLz5dDr9V4Z31G0HeLRwwAA27HDGDe4APs1o/FTg7Mns9Vmw77y\n07LUZrz1M1ByDHKPr4QY5B7fFQN5Vq+TzBNPPOH+uqSkBBaLBYmJiZI9fjkzMxNmsxk5OTmw2+1I\nTk7GwoULAQBFRUVYu3YtcnNzAaDTmu2AgABoNBqEhrIBpFuHZpk4aUTsuBvcj2MGuKSZiDyvxyTz\n0Ucf4eDBg/jf//1f934V160znU6H3//+94iPj/d4YCqVCtnZ2cjOzu70WmpqKlJTU7t9L1vgdKFD\ns0wMMiA93vkAurNWf5ytrXcvaWZthog8pcdpyJ49e1BfXw+dTodTp061q800NjZetBMAKYcwOQNC\n2kwIw5Ocf07OgEoQMC0hHMMH6FBea0XJWQu2/liLHaV1codLRP1Ej0nm7Nmz7h30+/fvBwAMGzYM\nb7/9NmJjY3H8+HFpIySPcG3KVGXNd27OPH+bU3Q4cPg/+4Gas0BjPQCR3ZmJyGN6TDIWi8W9X+XY\nsWMAgEmTJiE4OBjDhw9HY2OjtBGSpMTiAlz94x7U2wCT2Y76uiZ2ZyYij+kxyYSFheHQoUMoLy93\nz2RcrWTq6+s7bZgkH9NxQUCrnd2Zichjekwy1157LaqqqvDMM8+gtrYWYWFhGD58OACgtLS0U2dP\n8jGDDDAGRCDU0Yyo1iaEqlXszkxEHtPj6rJ7770XJ0+eRGlpKTQaDX75y19CEAQcOnQIZ8+elWQz\nJnmPMDkDI1p/wKFKO+BwANYWDD17HKIjWpYGf0TUvTFjxrj3K1rPd3xWq9XuZ8p89913cobXpR6T\nTGRkJJZkAtGvAAAXB0lEQVQuXepu6eLaFzNy5Ejk5uZCreb9e18mqFSYlTUT/1n9CcprbIi1/oQp\nZYUQdU0QUm+WOzwiauP77793f71o0SK0trZi6dKlF32P3W53P6BMDr3+qBocHNxu46Wfnx+CgoIk\n24xJ3rP1UDUqmkSoIKJCHYlC3YjOtRoiuiiHKKK8pgnV9c2yxjFlyhSsWrUK999/P8aMGYMdO3bg\n9ddfx/z589tdd++992Lt2rXu4yNHjuDhhx/GxIkTkZ6ejr/85S9obW3tczzypTdSjGNnmwB1INDi\n/D+HUR0JDGKzUaLecogi/rzjKPafdO4xm5UyCJmj5WtR88EHH2D16tVITExES0sLSkpKun08AODc\nqvLAAw/gt7/9LdatW4ezZ8/iscceQ3BwMH7xi1/0KRZOQwjDooMhButQr42AKTAMTfo4iJPS5Q6L\nyGccqKp3JxgA+OiHk2i29X0WcLnuuece9yrgwMCe20V9/PHHGDVqFO666y6oVCrExMRg/vz5+Pjj\nj/scC2cyhJ+Puhr//m8V9tZZoIYDFTY1dpTW4+bhEXKHRuQTlLbof9CgQZd0fWVlJfbs2YPx48e7\nzzkcDvj5+fU5FiYZgkoQEGw6iSjL+XvJLYDxcAsw/AZ5AyPyEaP0oRgzJBzfn6gFBGDOmMEICuj7\nL+jL1fHWWFcPfDx79qz760GDBuGmm27CG2+84fFYeLuMAABDm6ohAqhXBcHkF4zGRjMcoih3WEQ+\nQSUI+HXaMCy5fRSW3Tka/zPqarlDamfUqFE4cOAAjhw5Arvdjr///e84deqU+/U777wT+/btw6ZN\nm2C1WiGKIk6cOIGioqI+j80kQwCA9MFBMFhrYBX8oBZbUREQwUaZRJdAEAQMDtcgWifvIzO6KvBP\nmjQJ999/Px588EFMmTIFjY2NSE5Odr8+YMAA5ObmYtu2bZg6dSomTJiAJ5980iPP9xFEkR9X+aAm\nPU5WVuKtT/aipAHOTZkqFZJCgEd+PlbyTZlK+RnwvwP+DPjQMs/jTIYAODdlxl473LmU2WYFWpox\ntPQ7iMUFcodGRD6MhX9yS48Pg2OPEeXNzTCr1ChXRyL/RDOmiSIfYkZEl4UzGXJTCQKmDQlCrLUG\nFepIHAkaiG3+BtZmiOiycSZD7QiTM1BREw40wHnrTBfCh5gR0WXjTIbacdVmxIgo1AdoYWqyo/HE\nCdg3roOjaDtEh0PuEInIhzDJUCfp8WGIiwiEtVWE2t4CY40FO6pbIe76jAsBiOiSMMlQJypBgFbt\nhyhtAEJbLRBwvmkmwO7MRHRJmGSoS7Hh5zeUqZ1/Gqw1zuNBBpkiIiJfxMI/dSk9PgyiKOIrowCo\nHBCD9BDHxkOYOBWOou3OGc0gA4TJGXyCJhF1i0mGuqQSBAiCgEarCGjD8bk2HH76cKTv3glx12fO\ni46WAACfoElE3eJHUOpWx6XL5bUtnWsyrNEQ0UUwyVC33HUZAKIowmxtxduaZBToRsC9kJk1GiK6\nCN4uo26lx4cBcM5gzNZW50wmIAolgyYBdj2mDQmCMDmj0/tEh8O51Jl1G6IrHpMMdUslCJiWEA4A\nWLf3NNzP/9OFomLAeKjGxnT5PrG4gHUbIgKg4CTjcDiQl5eHwsJC2Gw2JCcnY8GCBQgJCel07fff\nf48tW7bAaDRCFEUMGTIEc+fOxciRI2WIvH+KDQ9EyRlLu+NusW5DROcp9h7Gpk2bsHfvXixduhSr\nV6+GKIpYsWJFl9c2NTVh5syZ+Nvf/oZ169bhxhtvxJIlS1BTU+PlqPuv9Pgw3JoYjqQBGtyaGO6+\nldaRQxSRH5aEtyMnXajdsG5DdMVS7EymoKAAc+bMQXR0NAAgOzsbTz75JEwmE6Kiotpdm5qa2u54\n+vTp+PDDD3Hs2DGMHz/eazH3Z21vnV3MjtI6bBOvBqJ1OGxtAUZeh5snT/RChESkRIpMMmazGSaT\nCXFxce5zMTEx0Gg0MBqNnZJMRxUVFWhoaMDQoUOlDpU6cC57FgBdKACgIlrTrujf1aIAIuq/FHm7\nzGJx3vvXarXtzgcHB7tf605dXR2WLVuG22+/HQMHDpQsRuqaq1YjiiLqW+w4UduC/OO1cJx/yrdr\nUYB4tIQNN4muAIqcyWg0GgDOGU1bTU1N7te6UlNTg5dffhkpKSmYO3eupDFS11y1mi/L69FobUWD\ntRVbf6wFAOftNi4KILqiKDLJaLVaREVFoaysDAaDs2hcXV0Ni8XiPu7ozJkzePHFFzFhwgRkZ2df\n0nh6vb7PMfeF3ON7OoZ5gwbBZDsKa2Wt+5zJFgC9Xo/Ga5PRWH7UfV53bbLHx79ccscg9/hKiEHu\n8cnzFJlkACAjIwObN29GUlISdDod8vLykJKS0mU95uTJk3jppZeQlpaGrKysSx6rqqrKEyFfFr1e\nL+v4UsUQFWCD1WZrd1xVVQUxaSwc584B3xYBAGrPnUOww4FT1dUeHf9Syf3vIPf4SohB7vFdMZBn\nKTbJZGZmwmw2IycnB3a7HcnJyVi4cCEAoKioCGvXrkVubi4AYPPmzaipqcHWrVvx6aefAgAEQcCC\nBQs6rTwj72jbLSA2PNB9LAoCCpp0MAZdB4O1BlMLt6EpIgIYdYOc4RKRRARRPF+RvYLx05v3Ysg/\nXoutu48CLc0AgFvqS3DHsFBYfi5vDU3ufwe5x1dCDHKP74qBPEuRq8uo/yqvbXE/CA1wPnEzIHaY\njBERkZSYZMirYsMDAV0IEBIGMTAITfo4vOl3TbtlzkTUfyi2JkP904Vajdbd2bn6ZJ17kUBPXQXY\n4ZnItzDJkFd129kZnR+S1hV2eCbyLfwISLLp2Mn5op2dXbiZk8incCZDsnHdOjPZAhAVYEN6fBgc\noogdpXXtlj6rhAuzHQwyuGcw7mMiUiwmGZKN69ZZ26Wr+cdr3W1oXM+vaVuncTfUvIQGm6zjEMmH\nSYYUpWNdpuOxoFJdcg2GdRwi+fDjHCmKIUyN+hY7TE021LfYYQhT9/0vZR2HSDZMMqQoIgTnejPB\nue5MbLP67LJ1rNuwjkPkNbxdRopSUdeCkEB/hLQ57qvLqeMQkWcwyZCixIYHugv+ruO+6m0dhwsE\niDyPSYYUpbvuzd7giQUCPS7BJrrCMMmQorTtCOB1HlggsKO07qJLsImuNLwXQOTigQUCPS3BJrrS\ncCZDdF53CwQupVbTVU2Jt9DoSsYkQ3RedwsELqVW01VNibfQ6ErGJEPUk0uo1XRVU+ItNLqSsSZD\n1JM+1mouq9s0UT/BmQxRD/q6mTM9PgyiKOIrYwMEAA7RudRZqroM9/uQkjDJEPXgcppytqUSBAiC\ngEarAwCw7WgtVIJ0dRk2BCUl4ccbIi/wal2GDUFJQZhkiLygq7qMQxSRf7wW6/aexr8OVMEhip4Z\njA1BSUF4u4zIC3pa2nzs3EnUxgV75BYaG4KSkjDJEHmBN5c2X0oNqe0igcZrkyEmjeUiAfIoJhki\nmUjRcfpStV0k0Fh+FGJtLRcJkEcxyRDJpO0ttJTYGFwfKXq/BQ0XCZDEmGSIZNL2Fpper0dVVRXy\nj9d6twXNIIN7mbP7mMiDmGSIFMTbLWjaLhLQXZuMuqSxko7XFVdd6FytCY7wKG4e7WcUm2QcDgfy\n8vJQWFgIm82G5ORkLFiwACEhIV1ev2/fPqxfvx6nT5/GwIEDMW/ePIwePdrLURP1zeXUafpyi63t\nIgGdXo/6qqrLC7wPXHWhloAAiDabMy7WhfoNxX5c2LRpE/bu3YulS5di9erVEEURK1as6PLaM2fO\nYNmyZbjzzjuRm5uLzMxMvPrqqzCZTF6Omqhv0uPDcGtiOJIGaHBrYnivngzqWgpdcsaCrT/WYkdp\nnRci9SDWhfo1xSaZgoICZGZmIjo6GhqNBtnZ2di3b1+XiWPXrl2Ij49Hamoq/Pz8kJqaivj4eOza\ntcv7gRP1gatOM39sDKYlhPdqRuLzXZ65ebRfU+TtMrPZDJPJhLi4OPe5mJgYaDQaGI1GREVFtbve\naDQiPj6+3bm4uDgYjfxERP2fEpZC94WrLhRYa4L9fE2G+g9FJhmLxfl/GK1W2+58cHCw+7W2mpub\nO12r1WpRWVkpXZBECtFVNwFf4qoLRej1sMhQEyJpKTLJaDQaAM4ZTVtNTU3u19oKCgrqdK3ZbO7y\nWqL+pqtuAkRKocgko9VqERUVhbKyMhgMzvuz1dXVsFgs7uO2DAYDSkpK2p0rKyvDdddd16vx9Hp9\n34PuA7nHV0IMco+vhBjkHl8JMcg9PnmeIpMMAGRkZGDz5s1ISkqCTqdDXl4eUlJSOtVjAGDKlCnY\nsmULiouLMX78eHz99dcoKyvDwoULezVWlYxTdNcmPDnJHYPc4yshBrnHV0IMco/vioE8S7FJJjMz\nE2azGTk5ObDb7UhOTnYnjaKiIqxduxa5ubkAnIsCfvOb3+Ddd9/FqlWrMGDAADz99NNdJiQiIvIe\nQRQ99RAL38VPb/wEK3cMco+vhBjkHt8VA3mWYvfJEBGR72OSISIiyTDJEBGRZJhkiIhIMkwyREQk\nGSYZIiKSDJMMERFJhkmGiIgkwyRDRESSYZIhIiLJMMkQEZFkmGSIiEgyTDJERCQZJhkiIpIMkwwR\nEUmGSYaIiCTDJENERJJhkiEiIskwyRARkWSYZIiISDJMMkREJBkmGSIikgyTDBERSYZJhoiIJMMk\nQ0REkmGSISIiyTDJEBGRZJhkiIhIMkwyREQkGX+5A+iK1WrFunXr8O233wIAJkyYgEceeQQBAQFd\nXv/ll19i+/btqKyshJ+fHxISEnDfffdh6NCh3gybiIg6UORM5u2338apU6ewfPlyLF++HCdPnkRu\nbm631zc3N+Puu+/Gm2++idWrVyMuLg4vv/wyrFarF6MmIqKOFJdkrFYrioqKcM899yA0NBShoaHI\nyspCYWEh7HZ7l++ZPn06rrvuOqjVavj7++Ouu+5CbW0tqqqqvBw9ERG1pbgkU1VVBZvNhri4OPe5\nuLg4WK3WXieNAwcOIDAwEAMHDpQqTCIi6gWv1mRWrlyJwsLCbl+fNWsWkpOTAQBardZ93vW1xWLp\ncYyqqiqsWrUK8+bNQ1BQUB8jJiKivvBqknnkkUcwb968bl9Xq9Xu2YrZbHYnF7PZDADQaDQX/fsr\nKyvx0ksv4fbbb8e0adM8FDUREV0uryaZwMBABAYGXvQavV4PtVqNsrIyXHvttQCA0tJSqNVq6PX6\nbt9XWlqKpUuXYvbs2ZgxY8YlxXWxv9cb5B5fCTHIPb4SYpB7fCXEIPf45HmKq8mo1Wqkpqbi/fff\nR319Perq6vD+++9jypQp8PfvOiceOXIEL774IubOnXvJCYaIiKQjiKIoyh1ERy0tLXjnnXfwzTff\nAAAmTpyIhx9+2L1P5uOPP0ZRURGWLVsGAFi8eDEOHz6MwMBAuL4dQRCQk5ODkSNHyvNNEBGRMpMM\nERH1D4q7XUZERP0HkwwREUmGSYaIiCTDJENERJJRZBdmOXz22WcoKipCRUUFIiMjsXz5cknHczgc\nyMvLQ2FhIWw2G5KTk7FgwQKEhIRIOq5LcXExPv/8c5SXl8NqtWLDhg1eGdclLy8P3333HUwmEzQa\nDcaMGYP77rsPOp3Oq3Fs3LgRRUVFaGhogL+/PxISEnDvvfciNjbWq3GIoojnnnsOR48exapVqxAZ\nGemVcVeuXImvvvoKarUaoihCEATcd999mD59ulfGd9m/fz/+8Y9/4MSJE1Cr1Zg0aRIeeeQRr4z9\n1FNPwWQyuY9bW1ths9nwpz/9yev/HfRHTDLnRUZG4o477kBlZeVFW994yqZNm7B3714sXboUOp0O\nK1euxIoVK5CTkyP52ACg0+kwY8YMtLS0YM2aNV4Zsy0/Pz8sXLgQQ4cORVNTE1asWIGVK1fit7/9\nrVfjuOmmm3DHHXdAo9HAarVi48aNeO2117BixQqvxvHJJ5/I1gYpLS0Njz76qCxjA8ChQ4fwl7/8\nBY899hjGjh0LURRRWVnptfFdWyFcNm7ciG+//ZYJxkN4u+y8CRMmYPz48V77BFlQUIDMzExER0dD\no9EgOzsb+/bta/eJSkqjR4/G5MmTERMT45XxOrrnnnsQGxsLlUqFkJAQzJw5EyUlJV6PQ6/Xu9sV\nORwOCIKAq666yqsxVFVVYfv27bj//vu9Oq5SbNiwATfffDPGjx8PPz8/+Pv7y/YL3uFwYOfOnbj5\n5ptlGb8/4kxGBmazGSaTqV2n6ZiYGGg0GhiNRkRFRckYnTwOHDgAg8Egy9hFRUVYt24dLBYLhgwZ\ngkWLFnltbFEUsXr1asybN69dU1hv+uabb7Bnzx6EhIRg3LhxmD17ttdmVS0tLTh27BhGjBiBZ555\nBiaTCUOHDsX999+P+Ph4r8TQ1p49e2A2m3HTTTd5fez+qt8nmd50fs7KyvJiRBe6SXf8pRIcHNyr\nTtP9ze7du5Gfn4/FixfLMn5qaipSU1NRV1eHd955B6+++ipefvllr4z96aefIiIiAuPGjcPZs2e9\nMmZbM2fORHZ2NkJDQ1FZWYmVK1dizZo1ePLJJ70yflNTE0RRRHFxMZ599lno9Xr861//wtKlS7F8\n+XKvJ978/HxMnjxZtoTfH/X7JNObzs/e5ro94+ou7dLU1NRjp+n+5uuvv8a6devwzDPPyH4PPCws\nDA8//DAWLFiAyspKDB48WNLxqqur8emnn+KVV14BAMjRfKPtbHrw4MF48MEH8fzzz+Pxxx/vtleg\nJ7lmTFOnTsWQIUMAAHfeeSf+9a9/4ccff0RKSorkMbhUV1fjwIEDWLJkidfGvBL0+yTTm87P3qbV\nahEVFYWysjL3LaLq6mpYLBbZbhnJYefOnXjvvffwzDPPIDExUe5wAMD99FVv3C46cuQI6uvr8dRT\nT0EURXeSefrpp5GVleX1FV5y0Gq1iI6O7nReEASvx5Kfn4/Y2FgkJCR4fez+rN8nmd5yOBxobW2F\n3W6HKIqw2WwA4G7K6WkZGRnYvHkzkpKSoNPpkJeXh5SUFK/VY1zfr+v7lPr77Wjr1q345z//id//\n/vey3HsHnDOHzz//HJMnT0ZoaCh++uknvPPOOxg5cqRX/h0mT56M0aNHu49/+uknLFq0CIsWLfJa\ny/vi4mKkpKRAq9Xi1KlTWL9+PcaNG+eVWYzL9OnT8dlnn2Hy5MnQ6/XYsmULAgICMGLECK/FYLfb\nUVhYiHvuucdrY14p2CDzvA8++AAffvhhp/P/+Mc/JBnP4XDg//7v/7Bz507Y7XYkJyfjF7/4hdf2\niezatQurVq3qdP6NN97wyi/YrKws+Pn5uZOaa49Gbm6u5GO7iKKIV155BaWlpWhpaUFISAjGjBmD\nu+++G6GhoV6Lw+Xs2bP41a9+5dV9MosXL0ZFRQVsNhvCwsIwfvx4zJkzx+vLqd9//33k5+e7H73+\nwAMPeHVWX1xcjDVr1uDNN99U3J0PX8ckQ0REkuE+GSIikgyTDBERSYZJhoiIJMMkQ0REkmGSISIi\nyTDJEBGRZJhkiIhIMkwyREQkGSYZuqI9//zzyMrK6rLbAxH1HZMMXdHkaMRIdCVhWxm6Yj3xxBOd\nnkQaHR3t9UcvE/Vn7MJMV6z09HTk5+ejpqYGw4cPx/Dhw73WoJToSsEkQ1esu+66CwcPHkRNTQ1S\nUlIwe/ZsuUMi6ndYkyEiIskwydAVTaVy/l/A4XDIHAlR/8TbZXRFcz36t7CwEE1NTYiLi0NaWpq8\nQRH1I5zJ0BXtjjvuwLBhw1BXV4dt27bhu+++kzskon6FS5iJiEgynMkQEZFkmGSIiEgyTDJERCQZ\nJhkiIpIMkwwREUmGSYaIiCTDJENERJJhkiEiIskwyRARkWT+PxsoQqObMMP6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b5dd7dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = simulate_exp_survival_data_X(N=100, censor_time=6, rate=0.9, beta=0.2)\n",
    "plot_cum_survival_X(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:42:36.917998",
     "start_time": "2016-07-29T17:42:36.557757"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stan/exp_survival_model.stan\n",
      "stan/pem_survival_model_unstructured.stan\n",
      "stan/pem_survival_model_gamma.stan\n",
      "stan/pem_survival_model_randomwalk_bspline_est_xi.stan\n",
      "stan/pem_survival_model_randomwalk.stan\n",
      "stan/pem_survival_model_randomwalk_bspline.stan\n",
      "stan/weibull_survival_model.stan\n"
     ]
    }
   ],
   "source": [
    "# fit exponential model to these data\n",
    "import stanity\n",
    "import survivalstan\n",
    "models = survivalstan.utils.read_files('stan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:43:46.394684",
     "start_time": "2016-07-29T17:42:36.919862"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n",
      "Ran in 66.599 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/ssd0/env/local/lib/python2.7/site-packages/stanity/psis.py:228: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  elif sort == 'in-place':\n",
      "/mnt/ssd0/env/local/lib/python2.7/site-packages/stanity/psis.py:246: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  bs /= 3 * x[sort[np.floor(n/4 + 0.5) - 1]]\n"
     ]
    }
   ],
   "source": [
    "weib_model = survivalstan.fit_stan_survival_model(df = df, \n",
    "                                                 formula = '~ X',\n",
    "                                                 event_col = 'event',\n",
    "                                                 time_col = 't',\n",
    "                                                 model_code = survivalstan.models.weibull_survival_model,\n",
    "                                                 chains = 4, \n",
    "                                                 iter = 5000,\n",
    "                                                 make_inits = survivalstan.make_weibull_survival_model_inits,\n",
    "                                                 model_cohort = 'exp simulated, weibull model'\n",
    "                                                ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:43:46.717975",
     "start_time": "2016-07-29T17:43:46.397081"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f4b8145a150>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7MAAAIDCAYAAADWo0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b59953090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(data=weib_model['coefs'], x = 'value', y = 'variable')\n",
    "plt.title('Posterior estimates of model coefficients, simulated data', size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:44:45.461386",
     "start_time": "2016-07-29T17:43:46.720031"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n",
      "Ran in 55.863 sec.\n"
     ]
    }
   ],
   "source": [
    "## try with exponential baseline hazard\n",
    "exp_model = survivalstan.fit_stan_survival_model(df = df,\n",
    "                         model_code = models['exp_survival_model.stan'],\n",
    "                         formula = '~ X',\n",
    "                         model_cohort = 'exp simulated, exp model',\n",
    "                         event_col = 'event',\n",
    "                         time_col = 't',\n",
    "                         chains = 4, \n",
    "                         iter = 5000,\n",
    "                         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:44:45.839383",
     "start_time": "2016-07-29T17:44:45.463159"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f4b5d6bb4d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABMIAAAIDCAYAAADmE7KLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmO/OVlbiMCYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b5995fa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs(models = [exp_model, weib_model])\n",
    "plt.title('Posterior estimates of model coefficients, simulated data', size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-29T17:44:45.865858",
     "start_time": "2016-07-29T17:44:45.841412"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'diff': 0.39328730440969473, 'se_diff': 0.52756534110618469}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stanity.loo_compare(weib_model['loo'], exp_model['loo'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Posterior predictive checking\n",
    "\n",
    "Basic strategy:\n",
    "\n",
    "1. Compute posterior estimates of y, assuming no censoring\n",
    "2. For each iteration, summarize proportion of population surviving to time `t`\n",
    "3. Summarize survival over iterations (requires selecting desired summary timepoints)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.490Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iter</th>\n",
       "      <th>index</th>\n",
       "      <th>ppred_t</th>\n",
       "      <th>model_cohort</th>\n",
       "      <th>ppred_event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.129399</td>\n",
       "      <td>exp simulated, exp model</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.745296</td>\n",
       "      <td>exp simulated, exp model</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.240160</td>\n",
       "      <td>exp simulated, exp model</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.167429</td>\n",
       "      <td>exp simulated, exp model</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.361147</td>\n",
       "      <td>exp simulated, exp model</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iter index   ppred_t              model_cohort ppred_event\n",
       "0     0     0  0.129399  exp simulated, exp model        True\n",
       "1     1     0  1.745296  exp simulated, exp model        True\n",
       "2     2     0  0.240160  exp simulated, exp model        True\n",
       "3     3     0  0.167429  exp simulated, exp model        True\n",
       "4     4     0  1.361147  exp simulated, exp model        True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## example for exponential model - extract `yhat_uncens` observations\n",
    "posterior_preds = survivalstan.utils.extract_params_long(models=[exp_model], element='yhat_uncens')\n",
    "posterior_preds.rename(columns = {'variable': 'index', 'value': 'ppred_t'}, inplace=True)\n",
    "posterior_preds['ppred_event'] = True\n",
    "posterior_preds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.493Z"
    },
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "##  for each iteration, summarize cumulative survival at each observed timepoint\n",
    "posterior = posterior_preds.groupby(['iter']).apply(\n",
    "    lambda row: survival_table_from_events(event_observed=row['ppred_event'], death_times=row['ppred_t']),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.497Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iter</th>\n",
       "      <th>event_at</th>\n",
       "      <th>removed</th>\n",
       "      <th>observed</th>\n",
       "      <th>censored</th>\n",
       "      <th>entrance</th>\n",
       "      <th>at_risk</th>\n",
       "      <th>Survival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.001416</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.025914</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.033246</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iter  event_at  removed  observed  censored  entrance  at_risk  Survival\n",
       "0     0  0.000000      0.0       0.0       0.0     100.0    100.0      1.00\n",
       "1     0  0.001416      1.0       1.0       0.0       0.0    100.0      1.00\n",
       "2     0  0.002300      1.0       1.0       0.0       0.0     99.0      0.99\n",
       "3     0  0.025914      1.0       1.0       0.0       0.0     98.0      0.98\n",
       "4     0  0.033246      1.0       1.0       0.0       0.0     97.0      0.97"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## rename variables\n",
    "## (should really be done by model cohort, but ok here since max is same for both groups)\n",
    "posterior['Survival'] = posterior['at_risk']/max(posterior['at_risk'])\n",
    "posterior.reset_index(inplace=True)\n",
    "posterior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.508Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iter</th>\n",
       "      <th>event_at</th>\n",
       "      <th>removed</th>\n",
       "      <th>observed</th>\n",
       "      <th>censored</th>\n",
       "      <th>entrance</th>\n",
       "      <th>at_risk</th>\n",
       "      <th>Survival</th>\n",
       "      <th>time_interval</th>\n",
       "      <th>interval_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.001416</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.025914</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.033246</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iter  event_at  removed  observed  censored  entrance  at_risk  Survival  \\\n",
       "0     0  0.000000      0.0       0.0       0.0     100.0    100.0      1.00   \n",
       "1     0  0.001416      1.0       1.0       0.0       0.0    100.0      1.00   \n",
       "2     0  0.002300      1.0       1.0       0.0       0.0     99.0      0.99   \n",
       "3     0  0.025914      1.0       1.0       0.0       0.0     98.0      0.98   \n",
       "4     0  0.033246      1.0       1.0       0.0       0.0     97.0      0.97   \n",
       "\n",
       "   time_interval  interval_t  \n",
       "0              1         0.0  \n",
       "1              1         0.0  \n",
       "2              1         0.0  \n",
       "3              1         0.0  \n",
       "4              1         0.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## define time intervals at which we want to summarize estimates\n",
    "time_bins = np.linspace(0, 10, 100)\n",
    "posterior['time_interval'] = np.digitize(posterior['event_at'], time_bins)\n",
    "posterior['interval_t'] = time_bins[posterior['time_interval']-1].astype(float)\n",
    "posterior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.512Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iter</th>\n",
       "      <th>time_interval</th>\n",
       "      <th>event_at</th>\n",
       "      <th>removed</th>\n",
       "      <th>observed</th>\n",
       "      <th>censored</th>\n",
       "      <th>entrance</th>\n",
       "      <th>at_risk</th>\n",
       "      <th>Survival</th>\n",
       "      <th>interval_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.096138</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.200744</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.10101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.293032</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.20202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.397739</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.30303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.491911</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.40404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iter  time_interval  event_at  removed  observed  censored  entrance  \\\n",
       "0     0              1  0.096138      1.0       1.0       0.0       0.0   \n",
       "1     0              2  0.200744      1.0       1.0       0.0       0.0   \n",
       "2     0              3  0.293032      1.0       1.0       0.0       0.0   \n",
       "3     0              4  0.397739      1.0       1.0       0.0       0.0   \n",
       "4     0              5  0.491911      1.0       1.0       0.0       0.0   \n",
       "\n",
       "   at_risk  Survival  interval_t  \n",
       "0     89.0      0.89     0.00000  \n",
       "1     81.0      0.81     0.10101  \n",
       "2     74.0      0.74     0.20202  \n",
       "3     67.0      0.67     0.30303  \n",
       "4     61.0      0.61     0.40404  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## for each interval, take last observation in the time interval\n",
    "ppsummary = posterior.copy()\n",
    "ppsummary = posterior.sort_values(['iter','time_interval','event_at']).groupby([\n",
    "        'iter','time_interval'], as_index=False).last()\n",
    "ppsummary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.514Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4b5d6b7690>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4kAAAH5CAYAAAAsk3u9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b5dda4c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## summarize over intervals \n",
    "sb.boxplot(data = ppsummary,\n",
    "           x = 'time_interval',\n",
    "           y = 'Survival',\n",
    "           fliersize=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.518Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_interval</th>\n",
       "      <th>interval_t</th>\n",
       "      <th>Survival_p10</th>\n",
       "      <th>Survival_p90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.10101</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.20202</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.30303</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.40404</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time_interval  interval_t  Survival_p10  Survival_p90\n",
       "0              1     0.00000          0.89          0.96\n",
       "1              2     0.10101          0.80          0.90\n",
       "2              3     0.20202          0.72          0.84\n",
       "3              4     0.30303          0.65          0.78\n",
       "4              5     0.40404          0.58          0.72"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sum_percentiles(df, percentile, by=['time_interval'], keep_cols = ['time_interval', 'interval_t','Survival']):\n",
    "    pdata = df.groupby(by, as_index=False).agg(lambda x: np.percentile(x, percentile))\n",
    "    pdata = pdata.loc[:, keep_cols]\n",
    "    return(pdata)\n",
    "\n",
    "p10 = sum_percentiles(ppsummary, percentile=10)\n",
    "p90 = sum_percentiles(ppsummary, percentile=90)\n",
    "\n",
    "ppsummary2 = pd.merge(p10, p90, on=['time_interval','interval_t'], suffixes = ['_p10','_p90'])\n",
    "ppsummary2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.521Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3cAAAH5CAYAAAAiFseXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b47f79f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cum_survival(event=df.event, t=df.t)\n",
    "_ = plt.fill_between(ppsummary2.interval_t,\n",
    "                     ppsummary2.Survival_p10,\n",
    "                     ppsummary2.Survival_p90,\n",
    "                     where= ppsummary2.Survival_p90 >=  ppsummary2.Survival_p10,\n",
    "                     facecolor='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "These checks can be useful, in two ways: \n",
    "\n",
    "1. In practice, we don't know the true shape of the baseline hazard.\n",
    "2. Also, it can help uncover problems in the process of data-simulation and/or estimation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![example2](https://raw.githubusercontent.com/jburos/biostan-examples/master/weibull-survival-model_files/figure-markdown_github/sim-plot-ppcheck-1.png \"Example of posterior predictive check\")\n",
    "\n",
    "[Link to analysis](https://github.com/jburos/biostan-examples/blob/master/weibull-survival-model.md)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![example1](https://raw.githubusercontent.com/jburos/biostan-examples/master/applied-survival-model_files/figure-markdown_github/wei-ppchecks-1.png \"Example of posterior predictive check\")\n",
    "\n",
    "[Link to analysis](https://github.com/jburos/biostan-examples/blob/master/applied-survival-model.md)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-28T01:27:42.302750",
     "start_time": "2016-07-28T01:27:42.281315"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Modeling the baseline hazard\n",
    "\n",
    "There are a variety of methods used to model the baseline hazard.\n",
    "\n",
    "Parametric:\n",
    "* Gamma\n",
    "* exponential\n",
    "* Weibull\n",
    "\n",
    "Semi- or non-parametric:\n",
    "* Gamma, Weibull or exponential *priors* on hazards\n",
    "* Gamma-process priors\n",
    "* random-walk priors\n",
    "* unconstrained piecewise hazard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Often we want to find a way to fit baseline hazards more flexibly, in order to better understand covariate effects.\n",
    "\n",
    "One approach to this is to use a \"binned likelihood\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.524Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3cAAAHnCAYAAAAbyqR8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b47e80410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_failure_times=df['t'].unique()\n",
    "for ftime in unique_failure_times:\n",
    "    plt.vlines(ftime, 0, 100, lw=0.5, linestyles=\"--\")\n",
    "plot_lifetimes(event_observed=df.event, lifetimes=df.t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.529Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>X</th>\n",
       "      <th>baseline_hazard</th>\n",
       "      <th>hazard</th>\n",
       "      <th>true_t</th>\n",
       "      <th>t</th>\n",
       "      <th>event</th>\n",
       "      <th>key</th>\n",
       "      <th>end_time</th>\n",
       "      <th>end_failure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3535</th>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.099262</td>\n",
       "      <td>0.004904</td>\n",
       "      <td>0.004904</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004904</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7535</th>\n",
       "      <td>75</td>\n",
       "      <td>True</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.099262</td>\n",
       "      <td>0.018886</td>\n",
       "      <td>0.018886</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004904</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7575</th>\n",
       "      <td>75</td>\n",
       "      <td>True</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.099262</td>\n",
       "      <td>0.018886</td>\n",
       "      <td>0.018886</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.018886</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.023054</td>\n",
       "      <td>0.023054</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.018886</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.023054</td>\n",
       "      <td>0.023054</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.023054</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index      X  baseline_hazard    hazard    true_t         t event  key  \\\n",
       "3535     35   True              0.9  1.099262  0.004904  0.004904  True    1   \n",
       "7535     75   True              0.9  1.099262  0.018886  0.018886  True    1   \n",
       "7575     75   True              0.9  1.099262  0.018886  0.018886  True    1   \n",
       "775       7  False              0.9  0.900000  0.023054  0.023054  True    1   \n",
       "707       7  False              0.9  0.900000  0.023054  0.023054  True    1   \n",
       "\n",
       "      end_time end_failure  \n",
       "3535  0.004904        True  \n",
       "7535  0.004904       False  \n",
       "7575  0.018886        True  \n",
       "775   0.018886       False  \n",
       "707   0.023054        True  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## transform data \n",
    "dflong = survivalstan.prep_data_long_surv(df=df.reset_index(), event_col='event', time_col='t')\n",
    "dflong.sort_values(['t']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### unstructured baseline hazard\n",
    "\n",
    "Now that we have prepared our data in the right format, we are ready to fit the `PEM` model.\n",
    "\n",
    "The first model we will consider is what I call an \"unstructured\" baseline hazard, since each timepoint is estimated somewhat independently from one another. \n",
    "\n",
    "We have an overall average hazard, called `log_baseline_mu`, and the hazard for each time period is estimated as an offset from this overall mean. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Here is our parameters block:\n",
    "\n",
    "```\n",
    "parameters {\n",
    "  vector[T] log_baseline_raw; // unstructured baseline hazard for each timepoint t\n",
    "  vector[M] beta;         // beta for each covariate\n",
    "  real<lower=0> baseline_sigma;\n",
    "  real log_baseline_mu;\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "and, our transformed parameters block:\n",
    "\n",
    "```\n",
    "transformed parameters {\n",
    "  vector[N] log_hazard;\n",
    "  vector[T] log_baseline;     // unstructured baseline hazard for each timepoint t\n",
    "  \n",
    "  log_baseline = log_baseline_raw + log_t_dur;  // adjust for duration of each time period\n",
    "  \n",
    "  for (n in 1:N) {\n",
    "    log_hazard[n] = log_baseline_mu + log_baseline[t[n]] + x[n,]*beta;\n",
    "  }\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Our model block simply puts a prior on the `log_baseline_mu` & `log_baseline_raw` parameters.\n",
    "\n",
    "```\n",
    "model {\n",
    "  beta ~ cauchy(0, 2);\n",
    "  event ~ poisson_log(log_hazard);\n",
    "  log_baseline_mu ~ normal(0, 1);\n",
    "  baseline_sigma ~ normal(0, 1);\n",
    "  log_baseline_raw ~ normal(0, baseline_sigma);\n",
    "}\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.531Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n",
      "Ran in 97.042 sec.\n"
     ]
    }
   ],
   "source": [
    "## fit unstructured-hazard model to these data\n",
    "pem_unstr = survivalstan.fit_stan_survival_model(df = dflong, \n",
    "                                                 formula = '~ X',\n",
    "                                                 event_col = 'end_failure',\n",
    "                                                 timepoint_end_col = 'end_time',\n",
    "                                                 sample_col = 'index',\n",
    "                                                 model_code = models['pem_survival_model_unstructured.stan'],\n",
    "                                                 chains = 4, \n",
    "                                                 iter = 1000,\n",
    "                                                 model_cohort = 'exp simulated, unstructured hazard',\n",
    "                                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Not surprisingly, we find little evidence that the hazard varies across timepoints. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.533Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7oAAAH5CAYAAACvV0KGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b47a1b150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs([pem_unstr], element='baseline_raw')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "How does the coefficient estimate from this `unstructured` model compare to that from our Weibull & Exponential models?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.535Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABOwAAAHyCAYAAABYoVHJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAECBCOwAAAAAoEAEdgAAAABQIP8fJVGYbCnkR6QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b42cdead0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs([pem_unstr, weib_model, exp_model])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Random-walk baseline hazard\n",
    "\n",
    "An alternative to the unstructured baseline hazard is to use what's called a *random walk* baseline hazard. \n",
    "\n",
    "This is a slight modification from the previous baseline hazard, such that each time period has a hazard is an offset from (i.e. *borrows information from*) the hazard estimate from the the previous time period. \n",
    "\n",
    "There are several ways to specify a *random walk*, such as by imposing a correlation structure on the estimates of baseline hazards. Here we have written the baseline hazard very explicitly in our model block, as follows:\n",
    "\n",
    "```\n",
    "\n",
    "model {\n",
    "  # [ ... some code omitted ... ] \n",
    "  log_baseline_raw[1] ~ normal(0, 1);\n",
    "  for (i in 2:T) {\n",
    "      log_baseline_raw[i] ~ normal(log_baseline_raw[i-1], baseline_sigma);\n",
    "  }\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.537Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n",
      "Ran in 552.588 sec.\n"
     ]
    }
   ],
   "source": [
    "## fit randomwalk-prior model to these data\n",
    "pem_randomwalk = survivalstan.fit_stan_survival_model(df = dflong, \n",
    "                                                 formula = '~ X',\n",
    "                                                 event_col = 'end_failure',\n",
    "                                                 timepoint_end_col = 'end_time',\n",
    "                                                 sample_col = 'index',\n",
    "                                                 model_code = models['pem_survival_model_randomwalk.stan'],\n",
    "                                                 chains = 1, \n",
    "                                                 iter = 5000,\n",
    "                                                 model_cohort = 'exp simulated, random walk prior hazard'\n",
    "                                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The resulting `baseline_raw` hazard estimates from this model reflect the structure we imposed on the sample -- recall that we only have a sample size of `n=100`.\n",
    "\n",
    "In this model, our baseline hazard has an overall mean (`log_baseline_mu`) and the deviance from that mean is allowed to vary from one timepoint to the next.\n",
    "\n",
    "Let's see what this looks like in practice:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.540Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7oAAAH5CAYAAACvV0KGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b47a36950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs([pem_randomwalk], element='baseline_raw')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now, a perhaps the more important question is how this variance in our baseline hazard impacts our coefficient estimates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.542Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQ8AAAHyCAYAAABF+jh7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQwAAAACgkPAQAAAAACj0/wBieguT/egG6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b565f3bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs([pem_unstr, pem_randomwalk, weib_model, exp_model])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In general, it appears that these models behave very similarly -- which is good. \n",
    "\n",
    "Let's compare to the \"True\" beta (the value used to simulate the data) of 0.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.545Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x7f4b759f0990>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQ8AAAHyCAYAAABF+jh7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhf4fPCXy7coXk7wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b5ff75a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs([pem_unstr, pem_randomwalk, weib_model, exp_model])\n",
    "plt.vlines(0.2, -200, 200, linestyles='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Compare model fit with unstructured baseline hazard to that with random walk.  Is there a difference between the two models according to PSIS-LOO? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-07-29T17:42:32.548Z"
    },
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'diff': 6.7186238987099252, 'se_diff': 5.0038264166095843}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stanity.loo_compare(pem_unstr['loo'], pem_randomwalk['loo'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "For this model, we wouldn't expect a strong difference between these two models, since the true hazard we used to simulate the data did not vary over time. In practice, hazards can and sometimes do vary over time. \n",
    "\n",
    "One of the challenges of survival analysis in practice, particularly with small sample sizes, is to *stabilize the estimates of the baseline hazard*, since our inferences for coefficient effects are multiplicative on this hazard.\n",
    "\n",
    "In a companion notebook, we work through a process for analyzing data from [TCGA](https://tcga-data.nci.nih.gov/tcga/tcgaCancerDetails.jsp?diseaseType=BLCA&diseaseName=Bladder Urothelial Carcinoma), focusing on the BLCA cohort."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": false,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  },
  "toc_position": {
   "height": "300px",
   "left": "1px",
   "right": "20px",
   "top": "106px",
   "width": "212px"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}