{ "cells": [ { "cell_type": "markdown", "id": "b27d13f8", "metadata": {}, "source": [ "# Sujet 6 : Autour du Paradoxe de Simpson\n", "\n", "### Marie-Félicia Béclin\n", "\n", "En 1972-1974, à Whickham, une ville du nord-est de l'Angleterre, située à environ 6,5 kilomètres au sud-ouest de Newcastle upon Tyne, un sondage d'un sixième des électeurs a été effectué afin d'éclairer des travaux sur les maladies thyroïdiennes et cardiaques (Tunbridge et al. 1977). Une suite de cette étude a été menée vingt ans plus tard (Vanderpump et al. 1995). Certains des résultats avaient trait au tabagisme et cherchaient à savoir si les individus étaient toujours en vie lors de la seconde étude. Par simplicité, nous nous restreindrons aux femmes et parmi celles-ci aux 1314 qui ont été catégorisées comme \"fumant 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.\n", "\n", "Les données sont disponibles dans ce fichier CSV. Vous trouverez sur chaque ligne si la personne fume ou non, si elle est vivante ou décédée au moment de la seconde étude, et son âge lors du premier sondage." ] }, { "cell_type": "markdown", "id": "c4aee39e", "metadata": {}, "source": [ "# Import des différentes bibliothèques utilisées" ] }, { "cell_type": "code", "execution_count": 72, "id": "aba6c422", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "plt.style.use('ggplot')" ] }, { "cell_type": "markdown", "id": "a5fe499c", "metadata": {}, "source": [ "### Consignes\n", "\n", "Représentez dans un tableau le nombre total de femmes vivantes et décédées sur la période en fonction de leur habitude de tabagisme. Calculez dans chaque groupe (fumeuses / non fumeuses) le taux de mortalité (le rapport entre le nombre de femmes décédées dans un groupe et le nombre total de femmes dans ce groupe). Vous pourrez proposer une représentation graphique de ces données et calculer des intervalles de confiance si vous le souhaitez. En quoi ce résultat est-il surprenant ?\n", "Reprenez la question 1 (effectifs et taux de mortalité) en rajoutant une nouvelle catégorie liée à la classe d'âge. On considérera par exemple les classes suivantes : 18-34 ans, 34-54 ans, 55-64 ans, plus de 65 ans. En quoi ce résultat est-il surprenant ? Arrivez-vous à expliquer ce paradoxe ? De même, vous pourrez proposer une représentation graphique de ces données pour étayer vos explications.\n", "\n", "Afin d'éviter un biais induit par des regroupements en tranches d'âges arbitraires et non régulières, il est envisageable d'essayer de réaliser une régression logistique. Si on introduit une variable Death valant 1 ou 0 pour indiquer si l'individu est décédé durant la période de 20 ans, on peut étudier le modèle Death ~ Age pour étudier la probabilité de décès en fonction de l'âge selon que l'on considère le groupe des fumeuses ou des non fumeuses. Ces régressions vous permettent-elles de conclure sur la nocivité du tabagisme ? Vous pourrez proposer une représentation graphique de ces régressions (en n'omettant pas les régions de confiance)." ] }, { "cell_type": "markdown", "id": "d77e6e53", "metadata": {}, "source": [ "# I) Import des données" ] }, { "cell_type": "code", "execution_count": 2, "id": "ea2c1273", "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", "
SmokerStatusAge
0YesAlive21.0
1YesAlive19.3
2NoDead57.5
3NoAlive47.1
4YesAlive81.4
\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" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_file = \"Subject6_smoking.csv\"\n", "data = pd.read_csv(data_file, skiprows=0)\n", "data.head()" ] }, { "cell_type": "markdown", "id": "0c568934", "metadata": {}, "source": [ "# II) Vérification et gestion des données manquantes" ] }, { "cell_type": "code", "execution_count": 68, "id": "856b90b2", "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame({ \"smoker\" : data[\"Smoker\"].apply(lambda val : 1 if val==\"Yes\" else 0),\n", " \"alive\" : data[\"Status\"].apply(lambda val : 1 if val==\"Alive\" else 0),\n", " \"age\": data[\"Age\"]})" ] }, { "cell_type": "code", "execution_count": 69, "id": "5ce60288", "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", "
smokeraliveage
count1314.0000001314.0000001314.000000
mean0.4429220.71917847.359361
std0.4969210.44957219.160667
min0.0000000.00000018.000000
25%0.0000000.00000031.300000
50%0.0000001.00000044.800000
75%1.0000001.00000060.600000
max1.0000001.00000089.900000
\n", "
" ], "text/plain": [ " smoker alive age\n", "count 1314.000000 1314.000000 1314.000000\n", "mean 0.442922 0.719178 47.359361\n", "std 0.496921 0.449572 19.160667\n", "min 0.000000 0.000000 18.000000\n", "25% 0.000000 0.000000 31.300000\n", "50% 0.000000 1.000000 44.800000\n", "75% 1.000000 1.000000 60.600000\n", "max 1.000000 1.000000 89.900000" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "id": "6a9d7d5b", "metadata": {}, "source": [ "Il n'y a pas de données manquantes." ] }, { "cell_type": "markdown", "id": "d201f4cf", "metadata": {}, "source": [ "# III) Première visualisation des données" ] }, { "cell_type": "code", "execution_count": 4, "id": "4b304b34", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Le nombre total de femmes dans l'étude est 1314\n", "Le nombre de femme morte pendant l'étude est 369\n", "Le nombre de femme encore en vie après l'étude est 945\n", "Le nombre de femme non-fumeuse 732\n", "Le nombre de femme fumeuse 582\n" ] } ], "source": [ "print(\"Le nombre total de femmes dans l'étude est \" +str(len(df[\"alive\"])))\n", "print(\"Le nombre de femme morte pendant l'étude est \"+ str(np.sum(df[\"alive\"] ==0)))\n", "print(\"Le nombre de femme encore en vie après l'étude est \"+ str(np.sum(df[\"alive\"])))\n", "print(\"Le nombre de femme non-fumeuse \"+ str(np.sum(df[\"smoker\"] ==0)))\n", "print(\"Le nombre de femme fumeuse \"+ str(np.sum(df[\"smoker\"])))" ] }, { "cell_type": "code", "execution_count": 5, "id": "cc456fe9", "metadata": {}, "outputs": [], "source": [ "df_smoker = df[df[\"smoker\"] ==1]\n", "df_nonsmoker = df[df[\"smoker\"] ==0]\n", "df_alive = df[df[\"alive\"] ==1]\n", "df_dead = df[df[\"alive\"] ==0]" ] }, { "cell_type": "markdown", "id": "e91fd4c9", "metadata": {}, "source": [ "On définit une fonction qui calcul de taux de mortalité pour n'importe quel jeux de données comportant la colone \"alive\"." ] }, { "cell_type": "code", "execution_count": 6, "id": "c88f2d79", "metadata": {}, "outputs": [], "source": [ "def mortalite(df):\n", " return (np.sum(df[\"alive\"] ==0)/len(df[\"alive\"]))" ] }, { "cell_type": "markdown", "id": "65dab701", "metadata": {}, "source": [ "On définit une fonction qui calcul de taux de fumeuses pour n'importe quel jeux de données comportant la colone \"smoker\"." ] }, { "cell_type": "code", "execution_count": 7, "id": "f5155e8f", "metadata": {}, "outputs": [], "source": [ "def smoke_rate(df):\n", " return (np.sum(df[\"smoker\"] ==1)/len(df[\"smoker\"]))" ] }, { "cell_type": "code", "execution_count": 8, "id": "1ab15069", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Pourcentage des participantes par âge des groupes fumeuses et non-fumeuses')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots( figsize =(15,7))\n", "ax.hist(df_smoker['age'],\n", " bins = int(np.max(df_smoker['age']) - np.min(df_smoker['age'])),\n", " density = True,\n", " alpha = 0.5, \n", " label =\"Fumeuses\" )\n", "\n", "ax.hist(df_nonsmoker['age'], \n", " bins = int(np.max(df_nonsmoker['age']) - np.min(df_nonsmoker['age'])), \n", " density = True, \n", " alpha = 0.5, \n", " label = \"Non Fumeuses\")\n", "plt.legend()\n", "plt.title(\"Pourcentage des participantes par âge des groupes fumeuses et non-fumeuses\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "c3e6886c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Pourcentage des participantes par âge des femmes en vie et décédées')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots( figsize =(15,7))\n", "ax.hist(df_alive['age'], \n", " bins = int(np.max(df_alive['age']) - np.min(df_alive['age'])),\n", " density = True,\n", " alpha = 0.5, \n", " label =\"En vie\" )\n", "ax.hist(df_dead['age'],\n", " bins = int(np.max(df_dead['age']) - np.min(df_dead['age'])), \n", " density = True, \n", " alpha = 0.5, \n", " label = \"Décédées\")\n", "plt.legend()\n", "plt.title(\"Pourcentage des participantes par âge des femmes en vie et décédées\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "0f85bd8e", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\BECLIN Marie-Felicia\\AppData\\Roaming\\Python\\Python37\\site-packages\\matplotlib\\cbook\\__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", " X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots( figsize =(15,7))\n", "ax.boxplot([df_alive[\"age\"],df_dead[\"age\"]])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "61890097", "metadata": {}, "source": [ "Les personnes décédées sont plus agées" ] }, { "cell_type": "code", "execution_count": 11, "id": "0a47e120", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'whiskers': [,\n", " ],\n", " 'caps': [,\n", " ],\n", " 'boxes': [],\n", " 'medians': [],\n", " 'fliers': [],\n", " 'means': []}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.boxplot(df_alive[\"age\"])" ] }, { "cell_type": "markdown", "id": "12d505cb", "metadata": {}, "source": [ "# IV) Première analyse" ] }, { "cell_type": "code", "execution_count": 12, "id": "45d218f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Le taux de mortalité des femmes non-fumeuses 0.31420765027322406\n", "Le taux de mortalité des femmes fumeuses 0.23883161512027493\n", "Le taux de fumeuses parmi les femmes décédées 0.37669376693766937\n", "Le taux de fumeuses parmi les femmes encore en vie 0.4687830687830688\n" ] } ], "source": [ "print(\"Le taux de mortalité des femmes non-fumeuses \"+ str(mortalite(df_nonsmoker)))\n", "print(\"Le taux de mortalité des femmes fumeuses \"+ str(mortalite(df_smoker)))\n", "print(\"Le taux de fumeuses parmi les femmes décédées \"+ str(smoke_rate(df_dead)))\n", "print(\"Le taux de fumeuses parmi les femmes encore en vie \"+ str(smoke_rate(df_alive)))" ] }, { "cell_type": "code", "execution_count": 13, "id": "5641b18b", "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", "
FumeuseNon Fumeuse
décédées23.88316231.420765
en vie76.11683868.579235
\n", "
" ], "text/plain": [ " Fumeuse Non Fumeuse\n", "décédées 23.883162 31.420765\n", "en vie 76.116838 68.579235" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tab = pd.DataFrame({\"Fumeuse\" : [mortalite(df_smoker)*100 , (1-mortalite(df_smoker))*100],\n", "\"Non Fumeuse\": [mortalite(df_nonsmoker)*100 , (1-mortalite(df_nonsmoker))*100]}, index = [\"décédées\", \"en vie\"])\n", "plt.bar([\"Fumeuse\", \"Non-fumeuse\"],tab.loc[\"décédées\"], label = 'décédées' )\n", "plt.bar([\"Fumeuse\", \"Non-fumeuse\"],tab.loc[\"en vie\"], bottom = tab.loc[\"décédées\"], label = 'en vie')\n", "plt.legend()\n", "tab.head()" ] }, { "cell_type": "markdown", "id": "35f303a9", "metadata": {}, "source": [ "Le taux de décés est plus important chez les non-fumeuses, c'est assez surprenant. " ] }, { "cell_type": "markdown", "id": "e0296de1", "metadata": {}, "source": [ "# V) Analyse par classe d'âge" ] }, { "cell_type": "code", "execution_count": 14, "id": "fa0f643c", "metadata": {}, "outputs": [], "source": [ "df[\"classe\"] = df[\"age\"].apply(lambda a : 1 if a<35 else 2 if a<55 else 3 if a <65 else 4)" ] }, { "cell_type": "code", "execution_count": 15, "id": "8401743f", "metadata": {}, "outputs": [], "source": [ "df_smoker = df[df[\"smoker\"] ==1]\n", "df_nonsmoker = df[df[\"smoker\"] ==0]\n", "df_alive = df[df[\"alive\"] ==1]\n", "df_dead = df[df[\"alive\"] ==0]" ] }, { "cell_type": "code", "execution_count": 16, "id": "970e42e6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, \"Mortalités des femmes non-fumeuses et fumeuses en fonction de leur classe d'âge\")" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "barWidth = 0.25\n", "mortalite_smoker = [mortalite(df_smoker[df_smoker[\"classe\"] == i]) for i in range(1,5)]\n", "mortalite_nonsmoker = [mortalite(df_nonsmoker[df_nonsmoker[\"classe\"] == i]) for i in range(1,5)]\n", "classe = [\"18-34 ans\", \"34-54 ans\", \"55-64 ans\", \"plus de 65 ans\"]\n", "r1 = range(len(mortalite_smoker))\n", "r2 = [x + barWidth for x in r1]\n", "fig, ax = plt.subplots(1,1,figsize=(15,10))\n", "plt.bar(r1,mortalite_smoker, width = barWidth,label = 'Fumeuses' )\n", "plt.bar(r2,mortalite_nonsmoker,width = barWidth, label = 'Non - Fumeuses')\n", "plt.xticks([r + barWidth / 2 for r in range(len(mortalite_smoker))],classe)\n", "plt.legend()\n", "plt.title(\"Mortalités des femmes non-fumeuses et fumeuses en fonction de leur classe d'âge\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "9bdc254c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "la proportion de femme agées de plus de 65 ans chez les fumeuses est 8%\n", "la proportion de femme agées de plus de 65 ans chez les non-fumeuses est 26%\n" ] } ], "source": [ "print(\"la proportion de femme agées de plus de 65 ans chez les fumeuses est \" + str(int(100*np.sum(df_smoker[\"classe\"]==4)/len(df_smoker[\"classe\"]))) +\"%\")\n", "print(\"la proportion de femme agées de plus de 65 ans chez les non-fumeuses est \"+ str(int(100*np.sum(df_nonsmoker[\"classe\"]==4)/len(df_nonsmoker[\"classe\"])))+\"%\")" ] }, { "cell_type": "markdown", "id": "932c7506", "metadata": {}, "source": [ "La mortalité des fumeuses est plus importante chez les jeunes femmes que chez les non-fumeuses. En revanche pour les plus de 65 ans la mortalité est presque la même. La proportion des femmes agées de plus 65 ans est plus grande dans la classe des non-fumeuse que des fumeuses, cela fait augmenter la mortalité dans ce groupe. Le taux de mortalité des non fumeuses est supérieur à celle des fumeuses car le groupe est plus agé. " ] }, { "cell_type": "markdown", "id": "113f39da", "metadata": {}, "source": [ "# VI) Analyse par classe d'âge" ] }, { "cell_type": "code", "execution_count": 37, "id": "6228d5f0", "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix" ] }, { "cell_type": "markdown", "id": "7c375e66", "metadata": {}, "source": [ "## Regression logistique sans la variable fumeur/non-fumeur" ] }, { "cell_type": "code", "execution_count": 31, "id": "9af4a729", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "82.97872340425532\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\BECLIN Marie-Felicia\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\utils\\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " return f(**kwargs)\n" ] } ], "source": [ "X = df[[\"smoker\",\"age\"]]\n", "y = df[[\"alive\"]]\n", "\n", "x_train, x_test, y_train, y_test = train_test_split(X, y, random_state = 0)\n", "\n", "modele_regLog = LogisticRegression(random_state = 0, solver = 'liblinear', multi_class = 'auto')\n", "\n", "modele_regLog.fit(x_train,y_train)\n", "\n", "\n", "precision = modele_regLog.score(x_test,y_test)\n", "print(precision*100)" ] }, { "cell_type": "code", "execution_count": 73, "id": "ba3de71d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 51 37]\n", " [ 19 222]]\n" ] } ], "source": [ "matrix_threshold = confusion_matrix(\n", " y_true=y_test, y_pred=modele_regLog.predict(x_test)\n", ")\n", "print(matrix_threshold)" ] }, { "cell_type": "code", "execution_count": 74, "id": "7e7a8b72", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, \"probabilité prédite de décés en fonction de l'age\")" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model_output = modele_regLog.predict_proba(x_test)\n", "probas_pred = model_output[:, np.where(modele_regLog.classes_ == 0 )[0]]\n", "\n", "fig, ax = plt.subplots(1,1,figsize = (15,10))\n", "\n", "plt.scatter(x_test[x_test['smoker'] ==1][\"age\"],probas_pred[x_test['smoker'] ==1], label = 'fumeur' )\n", "plt.scatter(x_test[x_test['smoker'] ==0][\"age\"],probas_pred[x_test['smoker'] ==0], label = 'non - fumeur' )\n", "plt.legend()\n", "plt.title(\"probabilité prédite de décés en fonction de l'age\")" ] }, { "cell_type": "markdown", "id": "32ab8709", "metadata": {}, "source": [ "La courbe de fumeur est au dessus. La probabilité de décès est donc plus élévée pour cette catégorie" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.7.8" } }, "nbformat": 4, "nbformat_minor": 5 }