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alonsosilvaallende/pharmacosmoking-cox-cph.ipynb

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pharmacosmoking-cox-cph.ipynb
{
"nbformat": 4,
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"metadata": {
"colab": {
"name": "pharmacoSmoking-Cox-CPH.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyPFGGYgSWEBLooGcjFAlsHa",
"include_colab_link": true
},
"kernelspec": {
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"display_name": "Python 3"
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"language_info": {
"name": "python"
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/alonsosilvaallende/d1b80730e9e837aac11e1e758671e95b/pharmacosmoking-cox-cph.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "MY2JWPDqQQvM"
},
"source": [
"!pip install -q lifelines"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Emg9o0o9QVLi"
},
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams.update({'xtick.labelsize' : 14,\n",
" 'ytick.labelsize' : 14})\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
"import statsmodels.api as sm\n",
"from lifelines import CoxPHFitter"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "r0HE7FHUUHh_"
},
"source": [
"Descargar datos"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 226
},
"id": "yDt-lf5-QXBQ",
"outputId": "9592399b-dc3b-4c7f-a94b-82d33e9efc9c"
},
"source": [
"pharmacoSmoking = sm.datasets.get_rdataset(\"pharmacoSmoking\", \"asaur\")\n",
"df = pharmacoSmoking.data\n",
"df.head()"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>ttr</th>\n",
" <th>relapse</th>\n",
" <th>grp</th>\n",
" <th>age</th>\n",
" <th>gender</th>\n",
" <th>race</th>\n",
" <th>employment</th>\n",
" <th>yearsSmoking</th>\n",
" <th>levelSmoking</th>\n",
" <th>ageGroup2</th>\n",
" <th>ageGroup4</th>\n",
" <th>priorAttempts</th>\n",
" <th>longestNoSmoke</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>21</td>\n",
" <td>182</td>\n",
" <td>0</td>\n",
" <td>patchOnly</td>\n",
" <td>36</td>\n",
" <td>Male</td>\n",
" <td>white</td>\n",
" <td>ft</td>\n",
" <td>26</td>\n",
" <td>heavy</td>\n",
" <td>21-49</td>\n",
" <td>35-49</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>113</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>patchOnly</td>\n",
" <td>41</td>\n",
" <td>Male</td>\n",
" <td>white</td>\n",
" <td>other</td>\n",
" <td>27</td>\n",
" <td>heavy</td>\n",
" <td>21-49</td>\n",
" <td>35-49</td>\n",
" <td>3</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>39</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>combination</td>\n",
" <td>25</td>\n",
" <td>Female</td>\n",
" <td>white</td>\n",
" <td>other</td>\n",
" <td>12</td>\n",
" <td>heavy</td>\n",
" <td>21-49</td>\n",
" <td>21-34</td>\n",
" <td>3</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>80</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>combination</td>\n",
" <td>54</td>\n",
" <td>Male</td>\n",
" <td>white</td>\n",
" <td>ft</td>\n",
" <td>39</td>\n",
" <td>heavy</td>\n",
" <td>50+</td>\n",
" <td>50-64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>87</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>combination</td>\n",
" <td>45</td>\n",
" <td>Male</td>\n",
" <td>white</td>\n",
" <td>other</td>\n",
" <td>30</td>\n",
" <td>heavy</td>\n",
" <td>21-49</td>\n",
" <td>35-49</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
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"</table>\n",
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],
"text/plain": [
" id ttr relapse ... ageGroup4 priorAttempts longestNoSmoke\n",
"0 21 182 0 ... 35-49 0 0\n",
"1 113 14 1 ... 35-49 3 90\n",
"2 39 5 1 ... 21-34 3 21\n",
"3 80 16 1 ... 50-64 0 0\n",
"4 87 0 1 ... 35-49 0 0\n",
"\n",
"[5 rows x 14 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "C0U9Y191UL3e"
},
"source": [
"Eliminar columnas redundantes"
]
},
{
"cell_type": "code",
"metadata": {
"id": "5YDzS-X5QdDh"
},
"source": [
"df = df.drop(columns=['id', 'ageGroup2', 'ageGroup4'])"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "4BLmgPhQQu6w",
"outputId": "a4d0e106-588e-4e1b-e9d0-57b03c7ec753"
},
"source": [
"df.head()"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ttr</th>\n",
" <th>relapse</th>\n",
" <th>grp</th>\n",
" <th>age</th>\n",
" <th>gender</th>\n",
" <th>race</th>\n",
" <th>employment</th>\n",
" <th>yearsSmoking</th>\n",
" <th>levelSmoking</th>\n",
" <th>priorAttempts</th>\n",
" <th>longestNoSmoke</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>182</td>\n",
" <td>0</td>\n",
" <td>patchOnly</td>\n",
" <td>36</td>\n",
" <td>Male</td>\n",
" <td>white</td>\n",
" <td>ft</td>\n",
" <td>26</td>\n",
" <td>heavy</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>patchOnly</td>\n",
" <td>41</td>\n",
" <td>Male</td>\n",
" <td>white</td>\n",
" <td>other</td>\n",
" <td>27</td>\n",
" <td>heavy</td>\n",
" <td>3</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>combination</td>\n",
" <td>25</td>\n",
" <td>Female</td>\n",
" <td>white</td>\n",
" <td>other</td>\n",
" <td>12</td>\n",
" <td>heavy</td>\n",
" <td>3</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>combination</td>\n",
" <td>54</td>\n",
" <td>Male</td>\n",
" <td>white</td>\n",
" <td>ft</td>\n",
" <td>39</td>\n",
" <td>heavy</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>combination</td>\n",
" <td>45</td>\n",
" <td>Male</td>\n",
" <td>white</td>\n",
" <td>other</td>\n",
" <td>30</td>\n",
" <td>heavy</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" ttr relapse grp ... levelSmoking priorAttempts longestNoSmoke\n",
"0 182 0 patchOnly ... heavy 0 0\n",
"1 14 1 patchOnly ... heavy 3 90\n",
"2 5 1 combination ... heavy 3 21\n",
"3 16 1 combination ... heavy 0 0\n",
"4 0 1 combination ... heavy 0 0\n",
"\n",
"[5 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TxVYoPD1Upvz"
},
"source": [
"OneHot encoding eliminando covariables consideradas como riesgo de base"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ug9q_DlARNbk"
},
"source": [
"df = pd.get_dummies(df, columns=[\"grp\", \"gender\", \"race\", \"employment\", \"levelSmoking\"])\n",
"# covariables de base\n",
"df = df.drop(columns=['grp_patchOnly', 'gender_Male', 'race_white', 'employment_ft', 'levelSmoking_light'])"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 820
},
"id": "dRgf1b2lQwHH",
"outputId": "fd9b14d0-554d-4593-af6b-c60b9c5d067e"
},
"source": [
"cph = CoxPHFitter()\n",
"cph.fit(df, duration_col='ttr', event_col='relapse')\n",
"\n",
"cph.print_summary() "
],
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/latex": "\\begin{tabular}{lrrrrrrrrrr}\n\\toprule\n{} & coef & exp(coef) & se(coef) & coef lower 95\\% & coef upper 95\\% & exp(coef) lower 95\\% & exp(coef) upper 95\\% & z & p & -log2(p) \\\\\ncovariate & & & & & & & & & & \\\\\n\\midrule\nage & -0.05 & 0.95 & 0.02 & -0.09 & -0.01 & 0.91 & 0.99 & -2.46 & 0.01 & 6.17 \\\\\nyearsSmoking & 0.02 & 1.02 & 0.02 & -0.02 & 0.06 & 0.98 & 1.06 & 0.87 & 0.39 & 1.37 \\\\\npriorAttempts & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.38 & 0.70 & 0.51 \\\\\nlongestNoSmoke & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & -0.64 & 0.52 & 0.94 \\\\\ngrp\\_combination & -0.59 & 0.55 & 0.22 & -1.03 & -0.16 & 0.36 & 0.85 & -2.69 & 0.01 & 7.12 \\\\\ngender\\_Female & 0.07 & 1.07 & 0.24 & -0.40 & 0.55 & 0.67 & 1.73 & 0.30 & 0.77 & 0.38 \\\\\nrace\\_black & 0.31 & 1.37 & 0.25 & -0.19 & 0.81 & 0.83 & 2.25 & 1.23 & 0.22 & 2.19 \\\\\nrace\\_hispanic & -0.28 & 0.76 & 0.48 & -1.23 & 0.67 & 0.29 & 1.96 & -0.57 & 0.57 & 0.82 \\\\\nrace\\_other & -1.00 & 0.37 & 1.02 & -3.00 & 0.99 & 0.05 & 2.70 & -0.99 & 0.32 & 1.62 \\\\\nemployment\\_other & 0.77 & 2.15 & 0.28 & 0.21 & 1.32 & 1.23 & 3.75 & 2.69 & 0.01 & 7.14 \\\\\nemployment\\_pt & 0.67 & 1.96 & 0.34 & 0.01 & 1.34 & 1.01 & 3.83 & 1.98 & 0.05 & 4.38 \\\\\nlevelSmoking\\_heavy & 0.06 & 1.06 & 0.26 & -0.46 & 0.57 & 0.63 & 1.77 & 0.21 & 0.83 & 0.27 \\\\\n\\bottomrule\n\\end{tabular}\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <th>model</th>\n",
" <td>lifelines.CoxPHFitter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>duration col</th>\n",
" <td>'ttr'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>event col</th>\n",
" <td>'relapse'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>baseline estimation</th>\n",
" <td>breslow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of observations</th>\n",
" <td>125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of events observed</th>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial log-likelihood</th>\n",
" <td>-372.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time fit was run</th>\n",
" <td>2021-08-04 13:59:04 UTC</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th style=\"min-width: 12px;\"></th>\n",
" <th style=\"min-width: 12px;\">coef</th>\n",
" <th style=\"min-width: 12px;\">exp(coef)</th>\n",
" <th style=\"min-width: 12px;\">se(coef)</th>\n",
" <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
" <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
" <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
" <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
" <th style=\"min-width: 12px;\">z</th>\n",
" <th style=\"min-width: 12px;\">p</th>\n",
" <th style=\"min-width: 12px;\">-log2(p)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>age</th>\n",
" <td>-0.05</td>\n",
" <td>0.95</td>\n",
" <td>0.02</td>\n",
" <td>-0.09</td>\n",
" <td>-0.01</td>\n",
" <td>0.91</td>\n",
" <td>0.99</td>\n",
" <td>-2.46</td>\n",
" <td>0.01</td>\n",
" <td>6.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>yearsSmoking</th>\n",
" <td>0.02</td>\n",
" <td>1.02</td>\n",
" <td>0.02</td>\n",
" <td>-0.02</td>\n",
" <td>0.06</td>\n",
" <td>0.98</td>\n",
" <td>1.06</td>\n",
" <td>0.87</td>\n",
" <td>0.39</td>\n",
" <td>1.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>priorAttempts</th>\n",
" <td>0.00</td>\n",
" <td>1.00</td>\n",
" <td>0.00</td>\n",
" <td>-0.00</td>\n",
" <td>0.00</td>\n",
" <td>1.00</td>\n",
" <td>1.00</td>\n",
" <td>0.38</td>\n",
" <td>0.70</td>\n",
" <td>0.51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>longestNoSmoke</th>\n",
" <td>-0.00</td>\n",
" <td>1.00</td>\n",
" <td>0.00</td>\n",
" <td>-0.00</td>\n",
" <td>0.00</td>\n",
" <td>1.00</td>\n",
" <td>1.00</td>\n",
" <td>-0.64</td>\n",
" <td>0.52</td>\n",
" <td>0.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>grp_combination</th>\n",
" <td>-0.59</td>\n",
" <td>0.55</td>\n",
" <td>0.22</td>\n",
" <td>-1.03</td>\n",
" <td>-0.16</td>\n",
" <td>0.36</td>\n",
" <td>0.85</td>\n",
" <td>-2.69</td>\n",
" <td>0.01</td>\n",
" <td>7.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gender_Female</th>\n",
" <td>0.07</td>\n",
" <td>1.07</td>\n",
" <td>0.24</td>\n",
" <td>-0.40</td>\n",
" <td>0.55</td>\n",
" <td>0.67</td>\n",
" <td>1.73</td>\n",
" <td>0.30</td>\n",
" <td>0.77</td>\n",
" <td>0.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>race_black</th>\n",
" <td>0.31</td>\n",
" <td>1.37</td>\n",
" <td>0.25</td>\n",
" <td>-0.19</td>\n",
" <td>0.81</td>\n",
" <td>0.83</td>\n",
" <td>2.25</td>\n",
" <td>1.23</td>\n",
" <td>0.22</td>\n",
" <td>2.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>race_hispanic</th>\n",
" <td>-0.28</td>\n",
" <td>0.76</td>\n",
" <td>0.48</td>\n",
" <td>-1.23</td>\n",
" <td>0.67</td>\n",
" <td>0.29</td>\n",
" <td>1.96</td>\n",
" <td>-0.57</td>\n",
" <td>0.57</td>\n",
" <td>0.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>race_other</th>\n",
" <td>-1.00</td>\n",
" <td>0.37</td>\n",
" <td>1.02</td>\n",
" <td>-3.00</td>\n",
" <td>0.99</td>\n",
" <td>0.05</td>\n",
" <td>2.70</td>\n",
" <td>-0.99</td>\n",
" <td>0.32</td>\n",
" <td>1.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>employment_other</th>\n",
" <td>0.77</td>\n",
" <td>2.15</td>\n",
" <td>0.28</td>\n",
" <td>0.21</td>\n",
" <td>1.32</td>\n",
" <td>1.23</td>\n",
" <td>3.75</td>\n",
" <td>2.69</td>\n",
" <td>0.01</td>\n",
" <td>7.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>employment_pt</th>\n",
" <td>0.67</td>\n",
" <td>1.96</td>\n",
" <td>0.34</td>\n",
" <td>0.01</td>\n",
" <td>1.34</td>\n",
" <td>1.01</td>\n",
" <td>3.83</td>\n",
" <td>1.98</td>\n",
" <td>0.05</td>\n",
" <td>4.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>levelSmoking_heavy</th>\n",
" <td>0.06</td>\n",
" <td>1.06</td>\n",
" <td>0.26</td>\n",
" <td>-0.46</td>\n",
" <td>0.57</td>\n",
" <td>0.63</td>\n",
" <td>1.77</td>\n",
" <td>0.21</td>\n",
" <td>0.83</td>\n",
" <td>0.27</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><br><div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <th>Concordance</th>\n",
" <td>0.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Partial AIC</th>\n",
" <td>769.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>log-likelihood ratio test</th>\n",
" <td>27.14 on 12 df</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-log2(p) of ll-ratio test</th>\n",
" <td>7.08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"<lifelines.CoxPHFitter: fitted with 125 total observations, 36 right-censored observations>\n",
" duration col = 'ttr'\n",
" event col = 'relapse'\n",
" baseline estimation = breslow\n",
" number of observations = 125\n",
"number of events observed = 89\n",
" partial log-likelihood = -372.58\n",
" time fit was run = 2021-08-04 13:59:04 UTC\n",
"\n",
"---\n",
" coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
"covariate \n",
"age -0.05 0.95 0.02 -0.09 -0.01 0.91 0.99\n",
"yearsSmoking 0.02 1.02 0.02 -0.02 0.06 0.98 1.06\n",
"priorAttempts 0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n",
"longestNoSmoke -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n",
"grp_combination -0.59 0.55 0.22 -1.03 -0.16 0.36 0.85\n",
"gender_Female 0.07 1.07 0.24 -0.40 0.55 0.67 1.73\n",
"race_black 0.31 1.37 0.25 -0.19 0.81 0.83 2.25\n",
"race_hispanic -0.28 0.76 0.48 -1.23 0.67 0.29 1.96\n",
"race_other -1.00 0.37 1.02 -3.00 0.99 0.05 2.70\n",
"employment_other 0.77 2.15 0.28 0.21 1.32 1.23 3.75\n",
"employment_pt 0.67 1.96 0.34 0.01 1.34 1.01 3.83\n",
"levelSmoking_heavy 0.06 1.06 0.26 -0.46 0.57 0.63 1.77\n",
"\n",
" z p -log2(p)\n",
"covariate \n",
"age -2.46 0.01 6.17\n",
"yearsSmoking 0.87 0.39 1.37\n",
"priorAttempts 0.38 0.70 0.51\n",
"longestNoSmoke -0.64 0.52 0.94\n",
"grp_combination -2.69 0.01 7.12\n",
"gender_Female 0.30 0.77 0.38\n",
"race_black 1.23 0.22 2.19\n",
"race_hispanic -0.57 0.57 0.82\n",
"race_other -0.99 0.32 1.62\n",
"employment_other 2.69 0.01 7.14\n",
"employment_pt 1.98 0.05 4.38\n",
"levelSmoking_heavy 0.21 0.83 0.27\n",
"---\n",
"Concordance = 0.66\n",
"Partial AIC = 769.17\n",
"log-likelihood ratio test = 27.14 on 12 df\n",
"-log2(p) of ll-ratio test = 7.08"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 597
},
"id": "gp8I6DywRFfq",
"outputId": "de47a247-51a9-40d3-bf21-822b604ee5c6"
},
"source": [
"fig, ax = plt.subplots(figsize=(10,8))\n",
"cph.plot(ax=ax)\n",
"ax.set_xlabel(\"log(HR) (95% CI)\", fontsize=14)\n",
"plt.title(\"Tasa de riesgo instantánea estimada (en escala logarítmica) para cada factor de\\nriesgo potencial de recaída a fumar usando el modelo de riesgos proporcionales de Cox\", fontsize=16)\n",
"plt.annotate(\"Fuente: Elaboración propia en base a datos 'pharmacoSmoking'\\nAutor: @alonsosilva\", (0,0), (-80,-60), xycoords='axes fraction', textcoords='offset points', va='top', fontsize=15)\n",
"plt.savefig(f\"{pd.to_datetime('today').strftime('%Y-%m-%d')}-pharmacoSmoking-Cox.svg\", bbox_inches='tight', format='svg')\n",
"# plt.show()"
],
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "aQXFb1Z8Vn3d",
"outputId": "47b24057-e190-4c74-a6f3-7977129a9f12"
},
"source": [
"from google.colab import files\n",
"files.download(f\"{pd.to_datetime('today').strftime('%Y-%m-%d')}-pharmacoSmoking-Cox.svg\")"
],
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/javascript": [
"download(\"download_d593d268-f497-48fb-ad56-bee6b5c69edb\", \"2021-08-04-pharmacoSmoking-Cox.svg\", 67642)"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
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