{
 "metadata": {
  "name": "",
  "signature": "sha256:60dbf24ff8d0fd0572d6acb97a5f6ab145de984b94a2ed322e7dfc44cddeea3c"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h1 class=\"alert alert-info\">HNSCC HPV- Cohort <small> <i class=\"icon-globe\"></i> Exploration of Subtypes in the Context of Clinical Variables</small></h1>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we are building some multivariate Cox models to explore the effect of the TP53-3p event within the context of clinical variables. Some of the results from this analysis show up in Supplemental Figure 7.  In the [Clinical_Covariates](../Analysis_Notebooks/Clinical_Covariates.ipynb) notebook, we do similar analysis but only looking at one covariate at a time. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Import Data and Packages  \n",
      "For full list of data and packages imported see the [Imports](../Analysis_Notebooks/Imports.ipynb) notebook."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import NotebookImport\n",
      "from Imports import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "importing IPython notebook from Imports.ipynb\n",
        "Populating the interactive namespace from numpy and matplotlib"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "changing to source dirctory"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "populating namespace with data"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Recreate Subtypes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53_mut = mut.features.ix['TP53'].ix[keepers_o].dropna()\n",
      "del_3p = cn.features.ix['Deletion'].ix['3p14.2'].ix[0].ix[keepers_o].dropna()\n",
      "\n",
      "combo = combine(p53_mut==1, del_3p==-1)\n",
      "combo = combo.map({'Lesion':'b', 'neither':'a', 'TP53':'c', 'both':'d'})\n",
      "two_hit = combo=='d'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Big Combined Model with Clincial Covariates"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Clinical model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clin_stage = clinical.processed.stage.ix[keepers_o].dropna()\n",
      "clin_stage.name = 'Clinical_Stage'\n",
      "lymph_stage = clinical.processed.lymph_stage.replace('n2', 'n2+').replace('n3', 'n2+').dropna()\n",
      "lymph_stage = lymph_stage == 'n2+'\n",
      "lymph_stage = lymph_stage.ix[keepers_o].fillna('nx')\n",
      "s4 = clin_stage == 'Stage iv'\n",
      "s4.name = 'Stage_IV'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "drinker = clinical.processed.drinker\n",
      "current_smoker = clinical.processed.smoker.dropna() == 'current smoker'\n",
      "current_smoker.name = 'current_smoker'\n",
      "non_smoker = clinical.processed.smoker.dropna() == 'lifelong non-smoker'\n",
      "non_smoker.name = 'non_smoker'\n",
      "recent_smoker = clinical.processed.smoker.dropna().isin(['current reformed smoker for < or = 15 years',\n",
      "                                                         'current smoker'])\n",
      "recent_smoker.name = 'recent_smoker'\n",
      "\n",
      "spread = clinical.processed.spread\n",
      "spread = spread.replace({'yes':True, 'no':False})\n",
      "invasion = clinical.processed.invasion\n",
      "invasion = invasion.replace({'yes':True, 'no':False})\n",
      "year = clinical.processed.year\n",
      "old = clinical.processed.old_age"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clinical_vars = pd.concat([drinker, current_smoker, non_smoker, recent_smoker,\n",
      "                           spread, invasion, year, old, s4,\n",
      "                           lymph_stage], 1).fillna(' missing')\n",
      "clinical_vars = clinical_vars.ix[keepers_o].dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Combined model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m2 = get_cox_ph(surv, two_hit, covariates=clinical_vars, print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "                        coef exp(coef) se(coef)     z      p\n",
        "feature               0.7868      2.20    0.307 2.560 0.0100\n",
        "drinker missing       0.7522      2.12    0.400 1.879 0.0600\n",
        "drinkerTrue           0.6583      1.93    0.459 1.434 0.1500\n",
        "recent_smoker missing 0.3422      1.41    0.486 0.704 0.4800\n",
        "recent_smokerTrue     0.7342      2.08    0.273 2.694 0.0071\n",
        "spread missing        0.0892      1.09    0.258 0.345 0.7300\n",
        "spreadTrue            0.5384      1.71    0.259 2.076 0.0380\n",
        "invasion missing      0.5071      1.66    0.297 1.710 0.0870\n",
        "invasionTrue          0.5237      1.69    0.262 2.003 0.0450\n",
        "yearpre_2000          0.6767      1.97    0.222 3.054 0.0023\n",
        "old_ageAge > 75       0.6564      1.93    0.292 2.244 0.0250\n",
        "\n",
        "Likelihood ratio test=66.8  on 11 df, p=4.9e-10  n= 250, number of events= 102 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m3 = get_cox_ph(surv, covariates=clinical_vars[['drinker','recent_smoker','spread','invasion','year', 'old_age']],\n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "                        coef exp(coef) se(coef)     z       p\n",
        "drinker missing       0.7391      2.09    0.400 1.847 6.5e-02\n",
        "drinkerTrue           0.6028      1.83    0.459 1.315 1.9e-01\n",
        "recent_smoker missing 0.6972      2.01    0.452 1.542 1.2e-01\n",
        "recent_smokerTrue     1.0121      2.75    0.255 3.963 7.4e-05\n",
        "spread missing        0.0932      1.10    0.257 0.363 7.2e-01\n",
        "spreadTrue            0.6928      2.00    0.257 2.692 7.1e-03\n",
        "invasion missing      0.4440      1.56    0.297 1.492 1.4e-01\n",
        "invasionTrue          0.5386      1.71    0.262 2.055 4.0e-02\n",
        "yearpre_2000          0.6896      1.99    0.222 3.109 1.9e-03\n",
        "old_ageAge > 75       0.6691      1.95    0.287 2.332 2.0e-02\n",
        "\n",
        "Likelihood ratio test=59.4  on 10 df, p=4.66e-09  n= 250, number of events= 102 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LR_test(m2, m3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "0.0065466476845842825"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cc = [colors[4], 'grey','grey', '#9ecae1','#9ecae1', 'grey','grey', '#9ecae1','#9ecae1',\n",
      "      'grey', '#9ecae1','#9ecae1']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(figsize=(6,5))\n",
      "ci = convert_robj(robjects.r.summary(m2)[7])\n",
      "haz = ci['exp(coef)']\n",
      "for j,h in enumerate(haz):\n",
      "    ax.scatter(h, j, marker='s', s=100, color=cc[j], \n",
      "               edgecolors=['black'], zorder=10)\n",
      "    ax.plot(*zip(*((ci.iloc[j]['lower .95'],j), (ci.iloc[j]['upper .95'],j))), \n",
      "            lw=3, ls='-', marker='o', dash_joinstyle='bevel', color=cc[j])\n",
      "ax.axvline(1, ls='--', color='black')\n",
      "ax.set_xscale('log')\n",
      "ax.set_xbound(.5,5)\n",
      "ax.set_ybound(-.5,len(ci.index) - .5)\n",
      "ax.set_xticks([.5, 1, 1.5, 2, 4])\n",
      "ax.set_xticklabels([.5, 1, 1.5, 2, 4])\n",
      "ax.set_yticks(range(len(ci.index)))\n",
      "ax.set_yticklabels(ci.index)\n",
      "ax.set_xlabel('Hazard Ratio')\n",
      "prettify_ax(ax)\n",
      "fig.tight_layout()\n",
      "fig.savefig(FIGDIR + 'mv_pb.pdf', tranparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Lh1BbwNF06As4mq5t4bPpEPoHjqZDCCHUeWEyQgghJHKYjBBCCIlcl/ql1+vX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\nw2SEEEKIcpiMEEIIUQ6TEUIIIcr9f4+RbickUnejAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8629110>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "In Patients Under 75"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clinical_vars = pd.concat([current_smoker, non_smoker, recent_smoker,\n",
      "                           spread, invasion, year, old, s4,\n",
      "                           lymph_stage], 1).fillna(' missing')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clinical_vars_y = clinical_vars.ix[ti(age < 75)].ix[keepers_o].dropna()\n",
      "clinical_vars_y = clinical_vars_y.replace(' missing', 'zzz')\n",
      "m1 = get_cox_ph(surv, covariates=clinical_vars_y, print_desc=True, interactions=False);\n",
      "m2 = get_cox_ph(surv, two_hit, covariates=clinical_vars_y, print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "                   coef exp(coef) se(coef)    z       p\n",
        "non_smokerTrue    1.182      3.26    0.605 1.95 5.1e-02\n",
        "non_smokerzzz     1.929      6.88    0.680 2.84 4.6e-03\n",
        "recent_smokerTrue 1.826      6.21    0.521 3.50 4.6e-04\n",
        "recent_smokerzzz     NA        NA    0.000   NA      NA\n",
        "spreadTrue        0.600      1.82    0.276 2.17 3.0e-02\n",
        "spreadzzz         0.299      1.35    0.282 1.06 2.9e-01\n",
        "invasionTrue      0.660      1.94    0.282 2.34 1.9e-02\n",
        "invasionzzz       0.379      1.46    0.327 1.16 2.5e-01\n",
        "yearpre_2000      0.963      2.62    0.240 4.02 5.8e-05\n",
        "\n",
        "Likelihood ratio test=54.7  on 8 df, p=5.05e-09  n= 220, number of events= 83 \n",
        "\n",
        "\n",
        "                   coef exp(coef) se(coef)    z       p\n",
        "feature           1.117      3.06    0.366 3.05 0.00230\n",
        "recent_smokerTrue 0.852      2.34    0.307 2.78 0.00550\n",
        "recent_smokerzzz  1.086      2.96    0.561 1.94 0.05300\n",
        "invasionTrue      0.665      1.94    0.282 2.36 0.01800\n",
        "invasionzzz       0.496      1.64    0.312 1.59 0.11000\n",
        "yearpre_2000      0.901      2.46    0.238 3.79 0.00015\n",
        "\n",
        "Likelihood ratio test=58.2  on 6 df, p=1.05e-10  n= 220, number of events= 83 \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(figsize=(6,4))\n",
      "ci = convert_robj(robjects.r.summary(m2)[7])\n",
      "haz = ci['exp(coef)']\n",
      "for j,h in enumerate(haz):\n",
      "    ax.scatter(h, j, marker='s', s=100, color=cc[j], \n",
      "               edgecolors=['black'], zorder=10)\n",
      "    ax.plot(*zip(*((ci.iloc[j]['lower .95'],j), (ci.iloc[j]['upper .95'],j))), \n",
      "            lw=3, ls='-', marker='o', dash_joinstyle='bevel', color=cc[j])\n",
      "ax.axvline(1, ls='--', color='black')\n",
      "ax.set_xscale('log')\n",
      "ax.set_xbound(.5,12)\n",
      "ax.set_ybound(-.5,len(ci.index) - .5)\n",
      "ax.set_xticks([.5, 1, 1.5, 2, 4,8])\n",
      "ax.set_xticklabels([.5, 1, 1.5, 2, 4,8])\n",
      "ax.set_yticks(range(len(ci.index)))\n",
      "ax.set_yticklabels(ci.index)\n",
      "ax.set_xlabel('Hazard Ratio')\n",
      "prettify_ax(ax)\n",
      "fig.tight_layout()\n",
      "fig.savefig(FIGDIR + 'mv_pa.pdf', tranparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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48OGPNm7cuOnu3btW169fn+Tr63tQIBDER0dHzwkLC/NJTU0d0aNHjwYHB4eL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\nRDtMRgghhGiHyQghhBDtMBkhhBCi3f8DXemHzVIPLroAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbdf7850>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clinical_vars_y = clinical_vars.ix[ti(age >= 75)].ix[keepers_o].dropna()\n",
      "clinical_vars_y = clinical_vars_y.replace(' missing', 'zzz')\n",
      "m1 = get_cox_ph(surv, covariates=clinical_vars_y, print_desc=True, interactions=False);\n",
      "m2 = get_cox_ph(surv, two_hit, covariates=clinical_vars_y, print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "                     coef exp(coef) se(coef)      z      p\n",
        "current_smokerTrue  1.236     3.442    0.745  1.660 0.0970\n",
        "current_smokerzzz  -0.980     0.375    0.953 -1.028 0.3000\n",
        "spreadTrue          0.588     1.801    0.867  0.678 0.5000\n",
        "spreadzzz          -1.642     0.194    0.837 -1.962 0.0500\n",
        "invasionTrue        1.214     3.368    0.818  1.485 0.1400\n",
        "invasionzzz         2.642    14.039    1.174  2.250 0.0240\n",
        "Stage_IV            0.962     2.616    0.367  2.617 0.0089\n",
        "\n",
        "Likelihood ratio test=16.4  on 7 df, p=0.0214  n= 30, number of events= 19 \n",
        "\n",
        "\n",
        "                     coef exp(coef) se(coef)      z      p\n",
        "current_smokerTrue  1.236     3.442    0.745  1.660 0.0970\n",
        "current_smokerzzz  -0.980     0.375    0.953 -1.028 0.3000\n",
        "spreadTrue          0.588     1.801    0.867  0.678 0.5000\n",
        "spreadzzz          -1.642     0.194    0.837 -1.962 0.0500\n",
        "invasionTrue        1.214     3.368    0.818  1.485 0.1400\n",
        "invasionzzz         2.642    14.039    1.174  2.250 0.0240\n",
        "Stage_IV            0.962     2.616    0.367  2.617 0.0089\n",
        "\n",
        "Likelihood ratio test=16.4  on 7 df, p=0.0214  n= 30, number of events= 19 \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(figsize=(6,4))\n",
      "ci = convert_robj(robjects.r.summary(m2)[7])\n",
      "haz = ci['exp(coef)']\n",
      "for j,h in enumerate(haz):\n",
      "    ax.scatter(h, j, marker='s', s=100, color=cc[j], \n",
      "               edgecolors=['black'], zorder=10)\n",
      "    ax.plot(*zip(*((ci.iloc[j]['lower .95'],j), (ci.iloc[j]['upper .95'],j))), \n",
      "            lw=3, ls='-', marker='o', dash_joinstyle='bevel', color=cc[j])\n",
      "ax.axvline(1, ls='--', color='black')\n",
      "ax.set_xscale('log')\n",
      "ax.set_xbound(.5,12)\n",
      "ax.set_ybound(-.5,len(ci.index) - .5)\n",
      "ax.set_xticks([.5, 1, 1.5, 2, 4,8])\n",
      "ax.set_xticklabels([.5, 1, 1.5, 2, 4,8])\n",
      "ax.set_yticks(range(len(ci.index)))\n",
      "ax.set_yticklabels(ci.index)\n",
      "ax.set_xlabel('Hazard Ratio')\n",
      "prettify_ax(ax)\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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U1KwPCgr6ua2tTQUA4M6dOx/b29vf0tDQaLC0tHyekpIykzpnVFRUiKGhYbmJ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x91aec10>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "In Ever-Smokers Under 75"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pts = ti(non_smoker.ix[keepers_o] != True).intersection(ti(old=='Age < 75'))\n",
      "clinical_vars_y = clinical_vars.ix[pts].fillna(' missing')\n",
      "m1 = get_cox_ph(surv, covariates=clinical_vars_y, print_desc=True, interactions=False);\n",
      "m2 = get_cox_ph(surv, two_hit, covariates=clinical_vars_y, print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "                         coef exp(coef) se(coef)      z       p\n",
        "recent_smoker missing  2.1670     8.732    0.700  3.096 0.00200\n",
        "recent_smokerTrue      2.0168     7.514    0.542  3.722 0.00020\n",
        "spread missing         0.5551     1.742    0.330  1.683 0.09200\n",
        "spreadTrue             0.8222     2.275    0.319  2.576 0.01000\n",
        "invasion missing       0.0698     1.072    0.355  0.196 0.84000\n",
        "invasionTrue           0.5971     1.817    0.317  1.883 0.06000\n",
        "yearpre_2000           0.8787     2.408    0.257  3.415 0.00064\n",
        "Stage_IV              -0.4363     0.646    0.198 -2.206 0.02700\n",
        "lymph_stagenx          2.1785     8.833    1.085  2.008 0.04500\n",
        "lymph_stageTrue        0.7148     2.044    0.406  1.762 0.07800\n",
        "\n",
        "Likelihood ratio test=52.2  on 10 df, p=1.03e-07  n= 181, number of events= 73 \n",
        "\n",
        "\n",
        "                         coef exp(coef) se(coef)      z      p\n",
        "feature                1.1710     3.225    0.456  2.567 0.0100\n",
        "recent_smoker missing  1.9795     7.239    0.725  2.732 0.0063\n",
        "recent_smokerTrue      1.6461     5.186    0.557  2.953 0.0031\n",
        "spread missing         0.6027     1.827    0.327  1.844 0.0650\n",
        "spreadTrue             0.7243     2.063    0.318  2.275 0.0230\n",
        "invasion missing       0.0992     1.104    0.353  0.281 0.7800\n",
        "invasionTrue           0.6279     1.874    0.320  1.963 0.0500\n",
        "yearpre_2000           0.7344     2.084    0.262  2.807 0.0050\n",
        "Stage_IV              -0.4144     0.661    0.198 -2.097 0.0360\n",
        "lymph_stagenx          1.6626     5.273    1.105  1.505 0.1300\n",
        "lymph_stageTrue        0.5123     1.669    0.404  1.269 0.2000\n",
        "\n",
        "Likelihood ratio test=60.5  on 11 df, p=7.38e-09  n= 181, number of events= 73 \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LR_test(m2, m1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "0.0039819954269985402"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(figsize=(6,4))\n",
      "ci = convert_robj(robjects.r.summary(m2)[7])\n",
      "haz = ci['exp(coef)']\n",
      "for j,h in enumerate(haz):\n",
      "    ax.scatter(h, j, marker='s', s=100, color='grey', \n",
      "               edgecolors=['black'], zorder=10)\n",
      "    ax.plot(*zip(*((ci.iloc[j]['lower .95'],j), (ci.iloc[j]['upper .95'],j))), \n",
      "            lw=3, ls='-', marker='o', dash_joinstyle='bevel', color='grey')\n",
      "ax.axvline(1, ls='--', color='black')\n",
      "ax.set_xscale('log')\n",
      "ax.set_xbound(.5,3.5)\n",
      "ax.set_ybound(-.5,len(ci.index) - .5)\n",
      "ax.set_xticks([.25,.5, 1, 1.5, 2, 4,8,16])\n",
      "ax.set_xticklabels([.25,.5, 1, 1.5, 2, 4,8,16])\n",
      "ax.set_yticks(range(len(ci.index)))\n",
      "ax.set_yticklabels(ci.index)\n",
      "ax.set_xlabel('Hazard Ratio')\n",
      "prettify_ax(ax)\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vli5deiA6Otq+rq5OPDY21jIoKMhbVla2wtXV9aCoqGjDP//8M6Hrax19SWw2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\nDiefSqW22tvbJwkEAiOiY+qudL6G9+zZ87WGhkathoZG7WeffXakL23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       "text": [
        "<matplotlib.figure.Figure at 0x86ab490>"
       ]
      }
     ],
     "prompt_number": 19
    }
   ],
   "metadata": {}
  }
 ]
}