{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sujet 6 : Autour du Paradoxe de Simpson" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Contexte de l'étude" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cette étude porte sur le [Paradoxe de Simpson](https://fr.wikipedia.org/wiki/Paradoxe_de_Simpson) (Simpson 1951, Undy 1903). Ce paradoxe est un paradoxe statistique \"dans lequel un phénomène observé de plusieurs groupes semble s'inverser lorsque les groupes sont combinés. Ce résultat qui semble impossible au premier abord est lié à des éléments qui ne sont pas pris en compte (comme la présence de variables non indépendantes ou de différences d'effectifs entre les groupes, etc.) est souvent rencontré dans la réalité, en particulier dans les sciences sociales et les statistiques médicales\" (Wikipédia). \n", "\n", "Pour représenter ce paradoxe, on utilisera les données d'un sondage des années 1970 d'une ville du nord-est de l'Angleterre sur un sixième des électeurs, complété par une seconde étude 20 ans plus tard (Vanderpump et al. 1995) sur les mêmes personnes. Le sondage initial avait été réalisé afin d'expliciter les travaux sur les maladies thyroïdiennes et cardiaques (Tunbridge et al. 1977). Le second sondage avait pour objectif de savoir si les individus étaient envore en vie, notamment au vu de leur tabagisme.\n", "\n", "Pour ce MOOC : \"Nous nous restreindrons aux femmes et parmi celles-ci aux 1314 qui ont été catégorisées comme \"fumant\n", "actuellement\" ou \"n'ayant jamais fumé\". Il y avait relativement peu de femmes dans le sondage initial ayant fumé et ayant arrêté depuis (162) et très peu pour lesquelles l'information n'était pas disponible (18). La survie à 20 ans a été déterminée pour l'ensemble des femmes du premier sondage\" (MOOC Recherche Reproductible)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importation des librairies python" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "import os\n", "import urllib.request\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Traitement des données\n", "\n", "Les donnés sont disponibles sur le GitLab du MOOC Reproductibilité. Par soucis d'accessibilité et pour éviter toute disparition de données suite à la fermeture du MOOC, on enregistrera les données récupérées de manière locale. Elles seront uniquement téléchargées si la copie locale n'existe pas.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "data_url = 'https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv?inline=false'\n", "data_file = 'simpson_paradox.csv'\n", "\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Chaque ligne des données représente une personne avec comme information:\n", "- Si la personne fume (Yes/No)\n", "- Si elle est vivante ou morte au moment de la 2ème étude (Alive/Dead)\n", "- Son âge au 1er sondage (arrondi à la 1ère décimale)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SmokerStatusAge
0YesAlive21.0
1YesAlive19.3
2NoDead57.5
3NoAlive47.1
4YesAlive81.4
5NoAlive36.8
6NoAlive23.8
7YesDead57.5
8YesAlive24.8
9YesAlive49.5
10YesAlive30.0
11NoDead66.0
12YesAlive49.2
13NoAlive58.4
14NoDead60.6
15NoAlive25.1
16NoAlive43.5
17NoAlive27.1
18NoAlive58.3
19YesAlive65.7
20NoDead73.2
21YesAlive38.3
22NoAlive33.4
23YesDead62.3
24NoAlive18.0
25NoAlive56.2
26YesAlive59.2
27NoAlive25.8
28NoDead36.9
29NoAlive20.2
............
1284YesDead36.0
1285YesAlive48.3
1286NoAlive63.1
1287NoAlive60.8
1288YesDead39.3
1289NoAlive36.7
1290NoAlive63.8
1291NoDead71.3
1292NoAlive57.7
1293NoAlive63.2
1294NoAlive46.6
1295YesDead82.4
1296YesAlive38.3
1297YesAlive32.7
1298NoAlive39.7
1299YesDead60.0
1300NoDead71.0
1301NoAlive20.5
1302NoAlive44.4
1303YesAlive31.2
1304YesAlive47.8
1305YesAlive60.9
1306NoDead61.4
1307YesAlive43.0
1308NoAlive42.1
1309YesAlive35.9
1310NoAlive22.3
1311YesDead62.1
1312NoDead88.6
1313NoAlive39.1
\n", "

1314 rows × 3 columns

\n", "
" ], "text/plain": [ " Smoker Status Age\n", "0 Yes Alive 21.0\n", "1 Yes Alive 19.3\n", "2 No Dead 57.5\n", "3 No Alive 47.1\n", "4 Yes Alive 81.4\n", "5 No Alive 36.8\n", "6 No Alive 23.8\n", "7 Yes Dead 57.5\n", "8 Yes Alive 24.8\n", "9 Yes Alive 49.5\n", "10 Yes Alive 30.0\n", "11 No Dead 66.0\n", "12 Yes Alive 49.2\n", "13 No Alive 58.4\n", "14 No Dead 60.6\n", "15 No Alive 25.1\n", "16 No Alive 43.5\n", "17 No Alive 27.1\n", "18 No Alive 58.3\n", "19 Yes Alive 65.7\n", "20 No Dead 73.2\n", "21 Yes Alive 38.3\n", "22 No Alive 33.4\n", "23 Yes Dead 62.3\n", "24 No Alive 18.0\n", "25 No Alive 56.2\n", "26 Yes Alive 59.2\n", "27 No Alive 25.8\n", "28 No Dead 36.9\n", "29 No Alive 20.2\n", "... ... ... ...\n", "1284 Yes Dead 36.0\n", "1285 Yes Alive 48.3\n", "1286 No Alive 63.1\n", "1287 No Alive 60.8\n", "1288 Yes Dead 39.3\n", "1289 No Alive 36.7\n", "1290 No Alive 63.8\n", "1291 No Dead 71.3\n", "1292 No Alive 57.7\n", "1293 No Alive 63.2\n", "1294 No Alive 46.6\n", "1295 Yes Dead 82.4\n", "1296 Yes Alive 38.3\n", "1297 Yes Alive 32.7\n", "1298 No Alive 39.7\n", "1299 Yes Dead 60.0\n", "1300 No Dead 71.0\n", "1301 No Alive 20.5\n", "1302 No Alive 44.4\n", "1303 Yes Alive 31.2\n", "1304 Yes Alive 47.8\n", "1305 Yes Alive 60.9\n", "1306 No Dead 61.4\n", "1307 Yes Alive 43.0\n", "1308 No Alive 42.1\n", "1309 Yes Alive 35.9\n", "1310 No Alive 22.3\n", "1311 Yes Dead 62.1\n", "1312 No Dead 88.6\n", "1313 No Alive 39.1\n", "\n", "[1314 rows x 3 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(url)\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On vérifir que toutes nos lignes sont bien remplies et que les âges sont cohérents" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SmokerStatusAge
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Smoker, Status, Age]\n", "Index: []" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ " data[data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ages minimaux et maximaux: [18.0, 89.9]\n" ] } ], "source": [ "print('Ages minimaux et maximaux: ' + str([data.Age.min(), data.Age.max()]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etudes\n", "\n", "### Décès en fonction des habitudes de tabagisme\n", "\n", "Le tableau suivant récapitule le nombre de femmes mortes ou vivantes selon sa relation au tabac." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AliveDeadMortality
Smoker4431390.239
Non-smoker5022300.314
\n", "
" ], "text/plain": [ " Alive Dead Mortality\n", "Smoker 443 139 0.239\n", "Non-smoker 502 230 0.314" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_death = pd.DataFrame(index=['Smoker', 'Non-smoker'], columns=['Alive', 'Dead'], data=[data[data.Smoker == 'Yes']['Status'].value_counts(), data[data.Smoker == 'No']['Status'].value_counts()])\n", "data_death['Mortality'] = round(data_death['Dead'] / (data_death['Dead'] + data_death['Alive']), 3) \n", "data_death" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.arange(2) # the label locations\n", "width = 0.35 # the width of the bars\n", "\n", "fig, ax = plt.subplots()\n", "ax.bar(x - width/2, data_death['Alive'], width, label='Alive')\n", "ax.bar(x + width/2, data_death['Dead'], width, label='Dead')\n", "ax2 = ax.twinx()\n", "ax2.plot(x, data_death['Mortality'], color='r', marker='o', label='Mortality')\n", "\n", "ax.set_ylabel('Number of women')\n", "ax2.set_ylabel('Mortality rate')\n", "ax.set_xticks(x)\n", "ax.set_xticklabels(['Smoker', 'Non-Smoker'])\n", "ax.legend()\n", "ax2.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A partir de ces graphiques et résultats il serait logique de conclure que les non fumeuses ont une mortalité plus importante (31%) par rapport aux fumeuses (24%) et que donc fumer aide à vivre longtemps. Même en regardant les intervales de confiance sur la condition (morte **0** ou vivante **1**) de la personne suivant son statut de fumeur nous indique que les fumeurs ont plus de chance de survie." ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.barplot(x='Smoker', y='Status', ci=95, data=data.replace('Alive', 1).replace('Dead', 0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mais est-ce vraiment le cas ? Nous avons regardé les données de manière globale sans rentrer dans les détails. Si l'on regarde l'âge des femmes suivant leur statut de fumeur un paradoxe commence à apparaître:" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.barplot(x='Smoker', y='Age', ci=95, data=data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La prochaine étape est donc d'étudier les données plus précisément, notamment suivant les tranches d'âges.\n", "\n", "## Décès liés au tabagisme suivant l'âge\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }