"
],
"text/plain": [
" tte AGE_DX EOD10_SZ MALIGCOUNT BENBORDCOUNT\n",
"label \n",
"0 6633 6633 6633 6633 6633\n",
"1 3333 3333 3333 3333 3333"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(['label']).count()"
]
},
{
"cell_type": "markdown",
"id": "99b36ff8",
"metadata": {},
"source": [
"### Censorhip\n",
"\n",
"Happens when the **time to event** is partially known.\n",
"\n",
"- **Not censored**: the event occured and the time is known.\n",
"- **Right-censored**: the survival duration is > the the observed. \n",
"- **Left-censored**: the survival duration is < then the observed duration.\n",
"- **Interval-censored**: the survival duration is within the range but not exactly known.\n",
"\n",
"> The data is **right-censored** because there are over 6,000 observations where the event didn't happen. It the cabe confirmed in the plot below."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "eff7bee6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12,4))\n",
"plt.subplot(1,2,1)\n",
"plt.title('Counting of Time to Event (tte)')\n",
"df['tte'].hist()\n",
"\n",
"plt.subplot(1,2,2)\n",
"plt.title('Counting based on LABEL')\n",
"df.groupby(['label']).count()['tte'].plot(kind='pie', label='');"
]
},
{
"cell_type": "markdown",
"id": "3c303cfa",
"metadata": {},
"source": [
"## Survival Function\n",
"\n",
"- Shows the percentage of the population that **did not** experienced the event until that time $t$.\n",
"- Shows the **survival probability** (probability of the event of interest happens) after time $t$: \n",
"\n",
"$$\\Large S(t) = Pr(T > t)$$\n",
"\n",
"- $T$: when the event occurs.\n",
"- $t$: any point in time during an observation.\n"
]
},
{
"cell_type": "markdown",
"id": "d0157fd0",
"metadata": {},
"source": [
"## Hazard Function\n",
"\n",
"- Shows the probability of that event happens at some time give the survival up to that time.\n",
"\n",
"$$\\Large h(t) = -\\frac{d}{dt} \\log(S(t))$$\n",
"\n",
"- **Hazard rate**: instantaneous rate of the event occuring"
]
},
{
"cell_type": "markdown",
"id": "b710bcbc",
"metadata": {},
"source": [
"## Survival Function/Curve Estimation\n",
"\n",
"$$\\Large S(t) = \\prod_{i:t_i \\leq t} \\left(1 - \\frac{d_i}{n_i} \\right)$$\n",
"\n",
"- $t_i$: duration time\n",
"- $d_i$: number of events that happened at time $t_i$\n",
"- $n_i$: number of individuals known to have survived up to time $t_i$"
]
},
{
"cell_type": "markdown",
"id": "744fddbe",
"metadata": {},
"source": [
"### Non-parametric\n",
"\n",
"- Does not assume underlying distribution.\n",
"- Describes the data better.\n",
"- It is not smooth/differentiable"
]
},
{
"cell_type": "markdown",
"id": "2d14c568",
"metadata": {},
"source": [
"#### Kaplan-Meier Model\n",
"most used"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f3f15b86",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kmf = lfl.KaplanMeierFitter()\n",
"kmf.fit(durations=df['tte'], event_observed=df['label'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "118d13a6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"kmf.plot_survival_function(at_risk_counts=True)\n",
"plt.ylabel('Survival Probability');"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c9651195",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Median survival time: 208.0\n"
]
}
],
"source": [
"# this measure might be wrong because more than 50% of the data is censored\n",
"print('Median survival time:', kmf.median_survival_time_)"
]
},
{
"cell_type": "markdown",
"id": "d96d0e36",
"metadata": {},
"source": [
"### Parametric \n",
"\n",
"- Usually gives more information because there is an underlying distribution\n",
"- Is necessary to assess the goodness-of-fit to avoid biases and misinterpretations\n",
"\n",
"\n",
"#### Weibull Model\n",
"\n",
"$$\\Large f(x; \\lambda,k) = \\frac{k}{\\lambda} \\left( \\frac{x}{\\lambda} \\right)^{k-1} e^{-(x/\\lambda)^k} \\qquad \\longrightarrow \\quad S(t) = e^{-(t/\\lambda)^\\rho}$$\n",
"\n",
"$$x \\geq 0, k > 0, \\lambda > 0$$\n",
"\n",
"- $k$ or $\\rho$: shape of the distribution\n",
" - $k < 1$: event rate decreases over time\n",
" - $k = 1$: event rate is constant\n",
" - $k > 1$: event rate increases over time\n",
" \n",
"- $\\lambda$: scale of the distribution (indicates when 63.2% of the population has experienced the event)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c5767d91",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wb = lfl.WeibullFitter()\n",
"\n",
"# Weibull needs non-zero tte\n",
"df['tte'] = df['tte'] + 1e-10\n",
"\n",
"wb.fit(durations=df['tte'], event_observed=df['label'])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6a3e8292",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lambda: 358.7938759636887\n",
"Rho: 0.7952285765619703\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Survival function of Weibull model / estimated parameters')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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yxo0bxyuvvHK6u3///tx1112nux9++GFeeumlPKf929/+xk8//QQ4d+cfPHjW+4LytXPnTmJinBdgzZ07l4EDBxYj+rMdPXqUN99883R3cnIyQ4cOLWAK702fPp3169eXyLzOVUkk/STOfIdlFEV7z2qRXFAhmJ7NavHtmn1kZeeU1tcYYwrRo0cP4uLiAMjJyeHgwYOsW7fu9PC4uDh69uyZ57RPPfUUffv2PS9xeit30q9Xrx5Tp04tkXmXpaRfEpdszgTGishnOK9IPOa+rrDUDO8czajJy/lx/X6ubFNqpw+M8RtPfrOO9cnHS3SerepV4e/X5P/+9p49ezJu3DgA1q1bR0xMDHv37uXIkSNUrFiRDRs2ANCnTx9SU1OpVasWkyZNom7dutx+++0MHDjwdE36+eefZ86cOQBMmTKFZs2anTVO5cqVSU1NLVIZ0tLSuP/++1mzZg1ZWVk88cQTDB48mHXr1jFy5EgyMjLIyclh2rRpPP7442zbto127drRr18/xowZw8CBA1m7di2TJk1i+vTpZGdns3btWh5++GEyMjKYPHkyYWFhzJ49mxo1avDOO+8wceJEMjIyaNasGZMnTyYhIYGZM2cyb948nnnmGaZNmwbAmDFjSElJoWLFirzzzjtcfPHFRVtBxeTNJZuf4rxj8iIRSRKRO0VktIiMdkeZjfPe0K047wq9r9SidV3R8kLqV7uAjxad03OHjDHnoF69eoSEhLB7927i4uLo3r07Xbt2ZdGiRcTHx9OyZUvGjRvH1KlTWb58OXfccQePPvponvOqUqUKS5cuZezYsTz00EMlFuOzzz7L5ZdfzrJly5gzZw6PPPIIaWlpTJgwgQcffJCEhATi4+OJioriueeeo2nTpiQkJPD888+fNa+1a9cyZcoUli5dyqOPPkrFihVZuXIl3bt356OPPgJgyJAhLFu2jFWrVtGyZUvee+89evTowaBBg3j++edJSEigadOmjBo1itdee43ly5fzwgsvcN99pZ42Tyu0pq+qNxYyXIExJRaRF4KDhFu7N+S5bzeyKvEosdHVzufXG1PmFFQjL009e/YkLi6OuLg4/vCHP7Bnzx7i4uKoWrUq9evX54cffqBfv34AZGdnU7du3r/Mb7zxxtP/T/16KAk//PADM2fO5IUXXgCcext2795N9+7defbZZ0lKSmLIkCE0b9680HlddtllREREEBERQdWqVbnmmmsAaNOmDatXrwacA8Njjz3G0aNHSU1NpX///mfNJzU1lbi4OK6//vrT/U6ePFkSxfWK396Re0u3hkyYt43xP2/h/ds7+zocYwLSqXb9NWvWEBMTQ3R0NC+++CJVqlTh8ssvZ8+ePSxatKjQ+Xhek37qc0hICDk5znk7VSUjI6PI8akq06ZN46KLLjqjf8uWLenatSv/+c9/6N+/P++++y5NmjQpcF5hYWGnPwcFBZ3uDgoKIisrC4Dbb7+d6dOnExsby6RJk5g7d+5Z88nJyaFatWokJCQUuTwlwW8fuFY5LIS7L2nCLxsPkJB41NfhGBOQevbsyaxZs6hRowbBwcHUqFGDo0ePsmjRIoYNG0ZKSsrppJ+ZmXnGiV5Pn3/++en/3bt3B5yrepYvXw7AjBkzyMzMLHJ8/fv357XXXuPUa2FXrlwJwPbt22nSpAkPPPAAgwYNYvXq1URERHDixIkif4enEydOULduXTIzM/nkk09O9/ecd5UqVWjcuDFffvkl4ByYVq1adU7fWxR+m/QBRvRoRPWKobz042Zfh2JMQGrTpg0HDx6kW7duZ/SrWrUqtWvXZurUqfzpT38iNjaWdu3anb7aJ7eTJ0/StWtXxo8fz8svvwzA3Xffzbx58+jSpQtLliyhUqVKRY7v8ccfJzMzk7Zt2xITE8Pjjz8OOAeXmJgY2rVrx8aNG7ntttuoWbMmPXv2JCYmhkceeaQYSwOefvppunbtSr9+/c44MTt8+HCef/552rdvz7Zt2/jkk0947733iI2NpXXr1syYMaNY31ccPnsxeqdOnbQk3pz1zvztPDt7Ax/e0YU+LYr9BjFj/M6GDRto2bKlr8MwpSivdSwiy1W1U3Hn6dc1fXBq+41qVuTpWevJtOv2jTGmQH6f9CuEBPHXq1qy9UAqU5bs9nU4xhgf+OCDD2jXrt0Zf2PGnNeLCv2G316946lfqwvp2awmL/24mUGx9aheqYKvQzLmvFBVe9ImMHLkSEaOHOnrMEpUaTW9+31NH5xLvB4f2IrUk1n889sNvg7HmPMiPDycQ4cOlVpyML5z6iUq4eHhJT7vclHTB7i4ThXuvqQJE+Zt49r29enRtJavQzKmVEVFRZGUlERxn1hryrZTr0ssaX5/9Y6n9Mxs+r8yHwG+e6g34aHBJTp/Y4zxtYC/esdTeGgw/7iuDTsP/cqrP2/xdTjGGFPmlKukD9CzWS2Gdoxi4vztrEs+5utwjDGmTCl3SR/g0ataUr1SBcZ9nkB6pr1W0RhjTimXSb96pQr8e2hbNu9P5YXvN/k6HGOMKTPKZdIHuOyi2tzarSHvLthB3FbvX8VmjDHlWblN+gB/vaolTWpV4uEvV3Hst6I/oc8YY8qbcp30L6gQzMvD2pFy4iSPT19rN7EYYwJeuU76ALHR1XjwiubMXJXMl/FJvg7HGGN8qtwnfYD7LmtGz2Y1eXzGWjbuK9mXRxtjjD8JiKQfHCS8Mqw9VS4IZcwnK0g7meXrkIwxxicCIukDREaEMX54O3YcTOMxa983xgSogEn6AD2a1uLBK1rw9co9fLYs0dfhGGPMeRdQSR9g7OXNuKR5Lf4+Yx0rdx/xdTjGGHNeBVzSDw4SXh3entpVwhj98XIOnEj3dUjGGHPeBFzSB+cxDRNv7cSx3zK57+MVZGTZu3WNMYHBq6QvIgNEZJOIbBWRP+cxvLqIfC0iq0VkqYjElHyoJatVvSo8PzSW+F1HePKbdb4OxxhjzotCk76IBANvAFcCrYAbRaRVrtH+CiSoalvgNmB8SQdaGq6Jrcc9fZrwyZLdfLrUXqpujCn/vKnpdwG2qup2Vc0APgMG5xqnFfAzgKpuBBqJyIUlGmkp+b/+F3NJ81r8bcZaFm075OtwjDGmVHmT9OsDntc3Jrn9PK0ChgCISBegIXDWyx1FZJSIxItIfFl5r2dwkPD6TR1oWLMSoz9ezraUVF+HZIwxpcabpC959Mt9Z9NzQHURSQDuB1YCZ932qqoTVbWTqnaKjIwsaqylpuoFoXxwe2dCgoQ7Ji3jcFqGr0MyxphS4U3STwKiPbqjgGTPEVT1uKqOVNV2OG36kcCOkgryfIiuUZGJt3Vi77F07pkcz8kse+OWMab88SbpLwOai0hjEakADAdmeo4gItXcYQB3AfNV1e+ebNaxYXVevD6WZTuP8Kepq+1RDcaYcieksBFUNUtExgLfA8HA+6q6TkRGu8MnAC2Bj0QkG1gP3FmKMZeqa2LrsetQGi/8sJkGNSvxh34tfB2SMcaUmEKTPoCqzgZm5+o3wePzIqB5yYbmO2Mua8auQ7/y6s9buLBKGDd3bejrkIwxpkR4lfQDjYjwjyFtOJSWwePT11KzUgUGxNT1dVjGGHPOAvIxDN4IDQ7ijZs6EBtdjQc+TbBr+I0x5YIl/QJcUCGY90d0pkHNioz6KJ51ycd8HZIxxpwTS/qFqF6pAh/d0YXK4SGMeH8Zuw/96uuQjDGm2Czpe6FetQuYfGcXsnJyuPX9JaScOOnrkIwxplgs6XupWe0I3hvRmQPHT3LLu0s4YnftGmP8kCX9IujYsDrvjujEjkNp3Pr+Eo79lunrkIwxpkgs6RdRz2a1ePuWjmzad4KRHywl7eRZjxgyxpgyy5J+MVx2cW1eHd6eVUnHuPPDZaRn2nN6jDH+wZJ+MV3Zpi4vXh/Lkh2HuWfycntAmzHGL1jSPwfXtq/Pc0PaMG9zCmM+WWnv2jXGlHmW9M/RsM4NeGpwa37asJ97P7YavzGmbLOkXwJu696Ipwe35ueNBxg9ebm18RtjyixL+iXk1u6N+Md1bZizKYV7LPEbY8ooS/ol6KauDXhuSBvmb0nh7o/iLfEbY8ocS/olbHiXBvzr921ZsPUgd364jN8yLPEbY8oOS/ql4IZO0bwwNJa4bYcYOWkpqXYDlzGmjLCkX0p+3zGKV4a1Y9nOI9z8zmJ7Vo8xpkywpF+KBrerz4RbOrJh3wmGTVzE/uPpvg7JGBPgLOmXsn6tLmTS7Z1JOvIb109YROJhex6/McZ3LOmfBz2a1WLK3d04np7J0AlxbNl/wtchGWMClCX986RddDU+H9UdVbjh7UWsTjrq65CMMQHIkv55dFGdCL4c3Z3K4SHc9M4Se9m6Mea8s6R/njWsWYkv7+lB3arhjHh/KbNWJ/s6JGNMALGk7wN1qobz5ejuxEZX5f5PV/Legh2+DskYEyC8SvoiMkBENonIVhH5cx7Dq4rINyKySkTWicjIkg+1fKlWsQKT7+xK/1Z1eHrWep79z3pyctTXYRljyrlCk76IBANvAFcCrYAbRaRVrtHGAOtVNRa4FHhRRCqUcKzlTnhoMG/c3IHbujfknf/u4KHPE+zRzMaYUhXixThdgK2quh1ARD4DBgPrPcZRIEJEBKgMHAbs2QNeCA4SnhzUmrpVL+Bf323kYOpJJtzakSrhob4OzRhTDnnTvFMfSPToTnL7eXodaAkkA2uAB1XVXiPlJRHh3kub8tINsSzdcZgbJixi77HffB2WMaYc8ibpSx79cjc+9wcSgHpAO+B1Ealy1oxERolIvIjEp6SkFDHU8m9Ihyjev70ziYd/5do3FrIm6ZivQzLGlDPeJP0kINqjOwqnRu9pJPCVOrYCO4CLc89IVSeqaidV7RQZGVncmMu13i0imXpvD4JFuOHtRXy/bp+vQzLGlCPeJP1lQHMRaeyenB0OzMw1zm7gCgARuRC4CNhekoEGkpZ1qzB9bE9a1Ilg9MfLmTh/G6p2ZY8x5twVmvRVNQsYC3wPbAC+UNV1IjJaREa7oz0N9BCRNcDPwJ9U9WBpBR0IakeE8/moblwVU5d/zN7IX75aQ2a2nSYxxpwbb67eQVVnA7Nz9Zvg8TkZ+F3JhmbCQ4N57cb2NKpVkTfmbCPxyK+8eVNHqla0K3uMMcVjd+SWcUFBwiP9L+b5oW1ZuuMwQ95ayK5Dab4Oyxjjpyzp+4nrO0Uz+c6uHErLYPAbC1m41VrPjDFFZ0nfj3RrUpMZY3pSOyKM295fynsLdtgJXmNMkVjS9zMNa1biq/t6csXFtXl61nr++OVq0jPt0Q3GGO9Y0vdDlcNCmHBLRx7q25xpK5IYNnGxvX/XGOMVS/p+KihIeKhvCybc0pEt+09wzWsLWLH7iK/DMsaUcZb0/dyAmDp8fV9PwkODGf72Yr5Yllj4RMaYgGVJvxy4qE4EM8f2pEvjGvzftNX85as11s5vjMmTJf1yolrFCkwa2Zl7L23Kp0t3c/2ERSQe/tXXYRljyhhL+uVISHAQfxpwMRNv7cjOQ2lc8/oC5m464OuwjDFliCX9cuh3revwzdhe1KkSzshJy3jlp832KkZjDGBJv9xqVKsSX9/Xk+va1+eVn7YwctIyjqRl+DosY4yPWdIvxy6oEMyL18fy7HUxLNp2iIGvLWB10lFfh2WM8SFL+uWciHBz14Z8Obo7AL9/K44PFtrjG4wJVJb0A0RsdDVm3d+LPi0iefKb9YyavJyjv1pzjzGBxpJ+AKleqQLv3NaJxwe2Yu6mA1w1/r8s33XY12EZY84jS/oBRkS4s1djpt3bg5DgIG54ezFvzt1qV/cYEyAs6QeotlHVmPVALwbE1OHf321ixAdLSTlx0tdhGWNKmSX9AFYlPJTXb2zPs9fFsHTHYa569b/E2ctZjCnXLOkHuFNX98wY25Mq4SHc/N4S/v3dRjKy7CXsxpRHlvQNABfXqcI39/fi+o5RvDl3G79/K45tKam+DssYU8Is6ZvTKlYI4d9DY5lwSwcSj/zKwFcXMGXJbrum35hyxJK+OcuAmLp892BvOjaszl+/XsOoycs5lGoneY0pDyzpmzzVqRrOR3d04bGrWzJvUwoDxv+XeZtTfB2WMeYcWdI3+QoKEu66pAnTx/SkesVQRry/lCdmrrMXtBjjxyzpm0K1qleFmWN7cXuPRkyK28mg1xewJumYr8MyxhSDV0lfRAaIyCYR2Soif85j+CMikuD+rRWRbBGpUfLhGl8JDw3miUGt+fCOLhz7LZNr31zIyz9uJjPbLu00xp8UmvRFJBh4A7gSaAXcKCKtPMdR1edVtZ2qtgP+AsxTVXuoSznUp0UkPzzUh0Gx9Rj/8xaufWMhG/cd93VYxhgveVPT7wJsVdXtqpoBfAYMLmD8G4FPSyI4UzZVrRjKy8PaMeGWjuw7ls6g1xby5tytZFmt35gyz5ukXx9I9OhOcvudRUQqAgOAafkMHyUi8SISn5JiV4L4uwExdfhhXG+uaFmbf3+3iaETFtkNXcaUcd4kfcmjX35361wDLMyvaUdVJ6pqJ1XtFBkZ6W2MpgyrWTmMN2/uwPjh7dhxMI2rxv+X9xbssKd2GlNGeZP0k4Boj+4oIDmfcYdjTTsBR0QY3K4+P47rTa9mtXh61nqGv7OYHQfTfB2aMSYXb5L+MqC5iDQWkQo4iX1m7pFEpCrQB5hRsiEaf1G7SjjvjujEv4e2ZcPe4wx4ZT5vz9tmbf3GlCGFJn1VzQLGAt8DG4AvVHWdiIwWkdEeo14H/KCqVr0LYCLCDZ2i+ekPfejdIpJ/fruRIW/FsWGvXeFjTFkgvnqYVqdOnTQ+Pt4n323OD1Vl9pp9/H3mWo7+msl9lzZlzOXNCAsJ9nVoxvgtEVmuqp2KO73dkWtKjYhwddu6/DiuD4Pa1ePVX7Zy9asLWL7riK9DMyZgWdI3pa56pQq8dEM7Jo3szG8Z2QydEMeT36wj7WSWr0MzJuBY0jfnzaUX1eb7cb25rVtDJsXtpP8r85mz6YCvwzImoFjSN+dV5bAQnhwcwxf3dCcsJIiRHyxjzCcr2H883dehGRMQLOkbn+jcqAazH7yEP/6uBT9t2M8VL87jw7idZNtNXcaUKkv6xmfCQoIZe3lzfhjXm/YNqvH3meu47s2FrN1jj202prRY0jc+17BmJT66owuv3tie5KPpDHp9AU9+s45UO9FrTImzpG/KBBFhUGw9fn64Dzd1bcCkuJ30fXEe363day9mN6YEWdI3ZUrVC0J55to2TLu3B9UrVWD0xyu468N4dh/61dehGVMuWNI3ZVKHBtX5ZmxPHr2qJYu2H6Lvy/N46cfN/JZh7+c15lxY0jdlVkhwEHf3bsIvD1/KgNZ1ePXnLfR9aR7fr9tnTT7GFJMlfVPm1akazqs3tuezUd2oHBbCPZOXM+KDZWy3F7YYU2SW9I3f6NakJrMe6MXfBrZi5a4j9H9lPv/6bqM9zsGYIrCkb/xKaHAQd/RqzC9/vJRBsfV5a+42rnhxHt+sSrYmH2O8YEnf+KXIiDBevCGWafd2p2blCtz/6UpufGcx65Ltxi5jCmJJ3/i1jg1rMHNsL56+NoZN+04w8LUF/HnaalJOnPR1aMaUSZb0jd8LDhJu7daQuX+8jDt7Nmbq8iQue2Eub83dRnqmXeJpjCdL+qbcqFoxlMcGtuKHcb3p1qQG//puI/1ense3a+yuXmNOsaRvyp0mkZV5d0RnJt/ZhYqhIdz7yQqGTVxsD3IzBkv6phy7pHkk/3mgF89cG8PWA6lc8/oC/m/qKg6csGf3m8BlSd+UayHBQdzSrSFz/ngpd/VqzNcr93DZ83MZ/9MWu77fBCRL+iYgVL0glEevbsUP4/pwSfNIXv5pM5e+MJcpS3aTlZ3j6/CMOW8s6ZuA0rhWJSbc2pFp93anQY2K/PXrNfR/ZT4/rt9vJ3tNQLCkbwJSx4Y1mDq6O2/f2hFVuPujeIa9vZiVu4/4OjRjSpUlfROwRIT+revw/bjePHNtDNsPpnHdm3Hc98lydhxM83V4xpQKr5K+iAwQkU0islVE/pzPOJeKSIKIrBOReSUbpjGlJ9Q92TvvkUt58IrmzN2UQr+X5vH3GWs5mGp39pryRQprxxSRYGAz0A9IApYBN6rqeo9xqgFxwABV3S0itVX1QEHz7dSpk8bHx59j+MaUvAMn0hn/0xY+W5ZIeEgQd13ShLsuaUxEeKivQzMGEVmuqp2KO703Nf0uwFZV3a6qGcBnwOBc49wEfKWquwEKS/jGlGW1I8J59ro2fP9Qb3q3iGT8z1u45N9zmDjfHutg/J83Sb8+kOjRneT289QCqC4ic0VkuYjclteMRGSUiMSLSHxKSkrxIjbmPGlWuzJv3dKRmWN70jaqGv+YvZE+z8/h48W7yMiyyzyNf/Im6Use/XK3CYUAHYGrgf7A4yLS4qyJVCeqaidV7RQZGVnkYI3xhbZR1fjoji58Nqob0dUr8tj0tfR9aR5fr0wiO8cu8zT+xZuknwREe3RHAcl5jPOdqqap6kFgPhBbMiEaUzZ0a1KTL0d354PbO1M5LIRxn6/iyvHz7Z29xq94k/SXAc1FpLGIVACGAzNzjTMDuEREQkSkItAV2FCyoRrjeyLCZRfXZtb9vXjjpg5k5Sj3TF7OtW8s5L9bUiz5mzIvpLARVDVLRMYC3wPBwPuquk5ERrvDJ6jqBhH5DlgN5ADvqura0gzcGF8KChKubluX/q0v5KuVexj/0xZufW8pnRtV56G+LejRtCYiebWMGuNbhV6yWVrskk1TnpzMyubzZYm8OWcb+46n06VRDR7q25zulvxNCTvXSzYt6RtTgtIzs/kiPpE35mxl//GTdGnsJv8mlvxNybCkb0wZlJ7p1vznnpn8ezSt5evQjJ+zpG9MGZaemc1nS3fz5txtHDhxkq6Na/BQ3xZ0b1rT16EZP2VJ3xg/kDv5d2tSgweusGYfU3SW9I3xI+mZ2Xy6dDdvucm/Q4NqjL28GZddVNuSv/GKJX1j/FB6ZjZfxicyYd529hz9jVZ1qzDmsmYMiKlDcJAlf5M/S/rG+LHM7Bymr9zDW3O3sf1gGk0iK3Hfpc0Y3K4eocH2ugtzNkv6xpQD2TnKt2v38sacbWzYe5yo6hdwT5+mXN8xivDQYF+HZ8oQS/rGlCOqypxNB3j9l62s2H2U2hFh3H1JE27q2oBKYYXeQG8CgCV9Y8ohVWXR9kO8MWcrC7ceolrFUEZ0b8Rt3RtSs3KYr8MzPmRJ35hybsXuI7w5Zxs/bdhPeGgQ13eM5q5LGtOwZiVfh2Z8wJK+MQFi64FU3pm/na9X7iErJ4cr29Tlnt5NaBtVzdehmfPIkr4xAebA8XQ+iNvJx4t3cSI9ix5Na3JPn6b0bl7LrvUPAJb0jQlQJ9Iz+WxpIu8t2MG+4+lcXCeCe/o0YWBbu9yzPLOkb0yAy8jKYeaqZCbO38bm/anUqxrOnZc0YVjnaCrbFT/ljiV9YwwAOTnK3M0HeHvedpbsOExEWAjDu0QzokcjoqpX9HV4poRY0jfGnCUh8SjvLdjB7DV7UVUGxNThzl6N6dCgurX7+zlL+saYfCUf/Y0PF+3k0yW7OZ6eRWx0Ne7s1ZgrY+pYu7+fsqRvjClU2skspq1I4oOFO9lxMI26VcO5rXsjburSgKoVQ30dnikCS/rGGK/l5DiPeXhvwQ7ith3igtBghnaMYmTPRjSJrOzr8IwXLOkbY4plffJx3l+4g5kJyWRk53DFxbUZ0aMRvZrVIsge71xmWdI3xpyTAyfS+Xjxbj5ZvItDaRk0qVWJW7s3ZGjHKCLCremnrLGkb4wpESezspm9Zi8fxu0iIfEolSoEM6RDFLd1b0jzCyN8HZ5xWdI3xpS4VYlH+WjRLr5ZnUxGVg49mtbktu6N6NuyNiF21Y9PWdI3xpSaQ6kn+Tw+kY8X7SL5WDr1qoZzc7eGDO8cbY949pHzkvRFZAAwHggG3lXV53INvxSYAexwe32lqk8VNE9L+sb4j6zsHH7eeICPFu1k4dZDVAgOYmBsXUZ0b0RsdDVfhxdQzjXpF/pgDhEJBt4A+gFJwDIRmamq63ON+l9VHVjcQIwxZVdIcBD9W9ehf+s6bNl/gsmLdzFteRJfrdhDTP0q3Ny1IYNi69nbvfyAN41zXYCtqrpdVTOAz4DBpRuWMaasan5hBE8NjmHxX6/gqcGtycpW/vLVGrr+42cem76G9cnHfR2iKYA3h+X6QKJHdxLQNY/xuovIKiAZ+KOqrss9goiMAkYBNGjQoOjRGmPKjIjwUG7r3ohbuzVkxe4jfLJkN1/EJ/Hx4t20b1CNm7o0YGDbelxQwV7sXpYU2qYvItcD/VX1Lrf7VqCLqt7vMU4VIEdVU0XkKmC8qjYvaL7Wpm9M+XP01wymrdjDlCW72JaSRpXwEIZ0iOLmrg3sss8SUupt+jg1+2iP7iic2vxpqnrc4/NsEXlTRGqp6sHiBmaM8T/VKlbgzl6NuaNnI5bsOMyUJbv5ZMkuJsXtpEujGtzcrQEDYuoQFmK1f1/xJukvA5qLSGNgDzAcuMlzBBGpA+xXVRWRLjjnCg6VdLDGGP8gInRrUpNuTWpyKLUVU5cnMWXpbh78LIHqFUMZ0iGKYZ2jaWG1//PO20s2rwJewblk831VfVZERgOo6gQRGQvcC2QBvwF/UNW4guZpzTvGBJacHCVu2yE+WbKLnzbsJzNbad+gGsM6RTMwtp695ctLdnOWMcbvHEo9ydcr9/D5skS2HEilYoVgrm5Tl2Gdo+nY0F70UhBL+sYYv6WqrEw8yhfLEvlmVTJpGdk0iazEsE7RDOkQRWSE3fWbmyV9Y0y5kHYyi/+s2csXyxKJ33WEkCDh8otrM6xzNH1aRNozf1yW9I0x5c7WA6l8GZ/ItBVJHEzNoHZEGEM7RjG0Y1TAv+zFkr4xptzKzM7hl40H+GJZInM2HSBHoX2Davy+QxTXtK0XkK96tKRvjAkI+4+nM33lHqatSGLz/lQqBAfRt1Vtft8hit4tIgPmRe+W9I0xAUVVWZd8nKnLk5i5KpnDaRnUqlyBQbH1+X3H+rSuV9XXIZYqS/rGmICVkZXDvM0pTFuexM8bnWv/L64Twe87RDG4fT1qR4T7OsQSZ0nfGGOAI2kZfLM6mWkr9rAq8SjBQULv5rUY0iGKfq0uJDy0fDz6wZK+McbksvXACaat2MPXK/aw73g6lcNCGBBTh8Ht6tGjaS2Cg/z35i9L+sYYk4/sHGXRtkNMT9jDd2v3kXoyi8iIMAa2rcu17erTNqqq3939a0nfGGO8kJ6ZzS8bDzB95R7mbkohIzuHxrUqMSi2Hte2r0/jWpV8HaJXLOkbY0wRHfs1k2/X7mV6wh6W7DiMKsRGVWVQu/pcE1u3TJ8AtqRvjDHnYO+x3/hmVTIzEpJZl3ycIIGezWoxKLYeA2LqEBFetm4As6RvjDElZOuBE0xfmcyMVXtIPPwbFUKCuOyiSAa2rccVLWtTsYLvH/9sSd8YY0qYqrJi91G+WZXMf9bsJeXESS4IDebylrUZ2KYul11c22eXgFrSN8aYUpSdoyzbeZhZq5P5ds0+DqVlUKlCMH1bXcjVberS56LI8/r6R0v6xhhznmRl57Bkh3sAWLuPo79mEhEWQr/WFzKwbV16NYukQkjpPgPIkr4xxvhAZnYOcdsOMWtVMt+v28fx9CyqXhBK/9YXcnXbevRoWrNUHgJnSd8YY3wsIyuHBVtTmLVqLz+s30/qySyqVwylf+s6XNmmbokeACzpG2NMGZKemc38zSnMWr2XXzYeIPWk8wugX6sLuapNHXo2q3VO5wAs6RtjTBmVnpnNgi0Hmb12Lz+u38+J9CwiwkJ4sG9z7rqkSbHmea5J3/cXnRpjTDkVHupc5dO31YVkZOWwcNtBvl2zlwur+O6OX0v6xhhzHjg3etXmsotq+zSOwHi/mDHGGMCSvjHGBBSvkr6IDBCRTSKyVUT+XMB4nUUkW0SGllyIxhhjSkqhSV9EgoE3gCuBVsCNItIqn/H+BXxf0kEaY4wpGd7U9LsAW1V1u6pmAJ8Bg/MY735gGnCgBOMzxhhTgrxJ+vWBRI/uJLffaSJSH7gOmFDQjERklIjEi0h8SkpKUWM1xhhzjrxJ+nm9QDL3HV2vAH9S1eyCZqSqE1W1k6p2ioyM9DJEY4wxJcWb6/STgGiP7iggOdc4nYDP3BcM1wKuEpEsVZ1eEkEaY4wpGYU+hkFEQoDNwBXAHmAZcJOqrstn/EnALFWdWsh8U4BdxYgZnAPLwWJOW9ZYWcomK0vZZGWBhqpa7KaSQmv6qpolImNxrsoJBt5X1XUiMtodXmA7fgHzLXbQIhJ/Ls+eKEusLGWTlaVssrKcO68ew6Cqs4HZufrlmexV9fZzD8sYY0xpsDtyjTEmgPhr0p/o6wBKkJWlbLKylE1WlnPks+fpG2OMOf/8taZvjDGmGCzpG2NMAPG7pO/tEz/LKhHZKSJrRCRBROLdfjVE5EcR2eL+r+7rOPMiIu+LyAERWevRL9/YReQv7nraJCL9fRN13vIpyxMissddNwkicpXHsDJZFhGJFpE5IrJBRNaJyINuf79bLwWUxR/XS7iILBWRVW5ZnnT7+369qKrf/OHcJ7ANaAJUAFYBrXwdVxHLsBOolavfv4E/u5//DPzL13HmE3tvoAOwtrDYcZ7IugoIAxq76y3Y12UopCxPAH/MY9wyWxagLtDB/RyBcyNlK39cLwWUxR/XiwCV3c+hwBKgW1lYL/5W0/f2iZ/+ZjDwofv5Q+Ba34WSP1WdDxzO1Tu/2AcDn6nqSVXdAWzFWX9lQj5lyU+ZLYuq7lXVFe7nE8AGnAci+t16KaAs+SnLZVFVTXU7Q90/pQysF39L+oU+8dMPKPCDiCwXkVFuvwtVdS84Gz7g25doFk1+sfvruhorIqvd5p9TP739oiwi0ghoj1Or9Ov1kqss4IfrRUSCRSQB53HzP6pqmVgv/pb0vXniZ1nXU1U74LyUZoyI9PZ1QKXEH9fVW0BToB2wF3jR7V/myyIilXHeZ/GQqh4vaNQ8+pX1svjlelHVbFVth/OQyi4iElPA6OetLP6W9L154meZpqrJ7v8DwNc4P+H2i0hdAPe/P72IJr/Y/W5dqep+d0fNAd7hfz+vy3RZRCQUJ0l+oqpfub39cr3kVRZ/XS+nqOpRYC4wgDKwXvwt6S8DmotIYxGpAAwHZvo4Jq+JSCURiTj1GfgdsBanDCPc0UYAM3wTYbHkF/tMYLiIhIlIY6A5sNQH8Xnt1M7oug5n3UAZLouICPAesEFVX/IY5HfrJb+y+Ol6iRSRau7nC4C+wEbKwnrx9VnuYpwVvwrnrP424FFfx1PE2JvgnKFfBaw7FT9QE/gZ2OL+r+HrWPOJ/1Ocn9eZODWTOwuKHXjUXU+bgCt9Hb8XZZkMrAFW4+yEdct6WYBeOM0Aq4EE9+8qf1wvBZTFH9dLW2ClG/Na4G9uf5+vF3sMgzHGBBB/a94xxhhzDizpG2NMALGkb4wxAcSSvjHGBBBL+sYYE0As6RtjTACxpG+MMQHk/wGu6twF9teXdgAAAABJRU5ErkJggg==\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('Lambda:', wb.lambda_)\n",
"print('Rho:', wb.rho_)\n",
"\n",
"ax = wb.survival_function_.plot()\n",
"ax.set_title(\"Survival function of Weibull model / estimated parameters\")"
]
},
{
"cell_type": "markdown",
"id": "e8b249be",
"metadata": {},
"source": [
"$\\rho$ is smaller than 1: the event rate (cancer incidence) decreases over time."
]
},
{
"cell_type": "markdown",
"id": "53c90e69",
"metadata": {},
"source": [
"### Comparing Several Models"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "37579fe1",
"metadata": {},
"outputs": [],
"source": [
"wb = lfl.WeibullFitter().fit(durations=df['tte'], event_observed=df['label'])\n",
"exp = lfl.ExponentialFitter().fit(durations=df['tte'], event_observed=df['label'])\n",
"log = lfl.LogNormalFitter().fit(durations=df['tte'], event_observed=df['label'])"
]
},
{
"cell_type": "markdown",
"id": "a9f6c534",
"metadata": {},
"source": [
"#### Akaike Information Criterion (AIC)\n",
"\n",
"- Estimates the prediction error and relative quality of statistical models\n",
"- The **less information the model loses, the higher its quality**. From `lifelines` documentation: *The model with the lowest AIC is desirable, since it’s a trade off between maximizing the log-likelihood with as few parameters as possible.*"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3a8f5b64",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Weilbull model: 44222.28063808024\n",
"Exponential model: 44474.95418984919\n",
"LogNormal model: 47666.55189603807\n"
]
}
],
"source": [
"print('Weilbull model:', wb.AIC_)\n",
"print('Exponential model:', exp.AIC_)\n",
"print('LogNormal model:', log.AIC_)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "61e725d9",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"needs_background": "light"
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"output_type": "display_data"
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"source": [
"_, axes = plt.subplots(2,1, figsize=(16,8))\n",
"aft.plot_partial_effects_on_outcome(covariates='MALIGCOUNT', values=np.arange(0, 1.01, 0.2), ax=axes[0])\n",
"aft.plot_partial_effects_on_outcome(covariates='BENBORDCOUNT', values=np.arange(0, 1.01, 0.2), ax=axes[1])"
]
},
{
"cell_type": "markdown",
"id": "ac090c18",
"metadata": {},
"source": [
"The `baseline` curve is the same one as estimated. The increase of `MALIGCOUNT` (counting of malignant tumors) affects the survival function in such a way that people has cancer sooner."
]
},
{
"cell_type": "markdown",
"id": "6d238a51",
"metadata": {},
"source": [
"### Cox Proportional Hazards Model (Cox-PH)\n",
"\n",
"#### Proportional hazards assumption\n",
"\n",
"All the individuals' hazards are proportional:\n",
"\n",
"$$\\Large h_A(t) = c \\cdot h_B(t) $$\n",
"\n",
"- There is a **baseline hazard** function where other hazards are **scaled**\n",
"- The **relative survival impact** of a varaible does not change with time (time-invariant)\n",
"- **baseline hazard** the risk for individuals at the baseline levels of covariates, i.e., the averages of covariates\n",
"\n",
"#### Cox-PH model: Regresses covariates on time-to-event/duration.\n",
"\n",
"$$\\Large h(t|x) = b_0 \\cdot exp\\left( \\sum^{n}_{i=1} b_i(x_i - \\bar{x_i}) \\right) $$\n",
"\n",
"- $b_0(t)$: population-level **baseline hazard** function\n",
"- $exp(...)$: linear relationship between covariates and the log of hazard. Note: Doesn't depend on time, that's why there is no $t$ in the equation\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "580ed82b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coxph = lfl.CoxPHFitter()\n",
"coxph.fit(df=df, duration_col='tte', event_col='label')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "b69894fa",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\helde\\miniconda3\\envs\\py39\\lib\\site-packages\\lifelines\\utils\\printer.py:62: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
" return summary_df[columns].to_latex(float_format=\"%.\" + str(self.decimals) + \"f\")\n"
]
},
{
"data": {
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"needs_background": "light"
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"output_type": "display_data"
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"source": [
"_, axes = plt.subplots(2,1, figsize=(16,8))\n",
"coxph.plot_partial_effects_on_outcome(covariates='MALIGCOUNT', values=np.arange(0, 1.01, 0.2), ax=axes[0])\n",
"coxph.plot_partial_effects_on_outcome(covariates='BENBORDCOUNT', values=np.arange(0, 1.01, 0.2), ax=axes[1])"
]
},
{
"cell_type": "markdown",
"id": "09730421",
"metadata": {},
"source": [
"#### Checking the Proportional Hazards Assumption"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "16582adb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The ``p_value_threshold`` is set at 0.05. Even under the null hypothesis of no violations, some\n",
"covariates will be below the threshold by chance. This is compounded when there are many covariates.\n",
"Similarly, when there are lots of observations, even minor deviances from the proportional hazard\n",
"assumption will be flagged.\n",
"\n",
"With that in mind, it's best to use a combination of statistical tests and visual tests to determine\n",
"the most serious violations. Produce visual plots using ``check_assumptions(..., show_plots=True)``\n",
"and looking for non-constant lines. See link [A] below for a full example.\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\helde\\miniconda3\\envs\\py39\\lib\\site-packages\\lifelines\\fitters\\mixins.py:108: FutureWarning: Index.__and__ operating as a set operation is deprecated, in the future this will be a logical operation matching Series.__and__. Use index.intersection(other) instead.\n",
" for variable in self.params_.index & (columns or self.params_.index):\n",
"C:\\Users\\helde\\miniconda3\\envs\\py39\\lib\\site-packages\\lifelines\\statistics.py:143: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
" return self.summary.to_latex()\n"
]
},
{
"data": {
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"\n",
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null_distribution
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chi squared
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<lifelines.CoxPHFitter: fitted with 9966 total...
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proportional_hazard_test
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-log2(p)
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AGE_DX
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km
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127.51
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<0.005
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95.81
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\n",
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rank
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BENBORDCOUNT
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0.69
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0.41
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1.30
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rank
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6.96
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0.01
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MALIGCOUNT
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12.96
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rank
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16.45
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<0.005
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14.29
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"text/latex": [
"\\begin{tabular}{llrrr}\n",
"\\toprule\n",
" & & test\\_statistic & p & -log2(p) \\\\\n",
"\\midrule\n",
"AGE\\_DX & km & 127.505541 & 1.439983e-29 & 95.809863 \\\\\n",
" & rank & 121.912576 & 2.412372e-28 & 91.743534 \\\\\n",
"BENBORDCOUNT & km & 0.693386 & 4.050151e-01 & 1.303952 \\\\\n",
" & rank & 0.137979 & 7.102984e-01 & 0.493503 \\\\\n",
"EOD10\\_SZ & km & 5.445903 & 1.961428e-02 & 5.671952 \\\\\n",
" & rank & 6.957959 & 8.344718e-03 & 6.904921 \\\\\n",
"MALIGCOUNT & km & 14.703542 & 1.258098e-04 & 12.956468 \\\\\n",
" & rank & 16.453643 & 4.985429e-05 & 14.291923 \\\\\n",
"\\bottomrule\n",
"\\end{tabular}\n"
],
"text/plain": [
"\n",
" null_distribution = chi squared\n",
"degrees_of_freedom = 1\n",
" model = \n",
" test_name = proportional_hazard_test\n",
"\n",
"---\n",
" test_statistic p -log2(p)\n",
"AGE_DX km 127.51 <0.005 95.81\n",
" rank 121.91 <0.005 91.74\n",
"BENBORDCOUNT km 0.69 0.41 1.30\n",
" rank 0.14 0.71 0.49\n",
"EOD10_SZ km 5.45 0.02 5.67\n",
" rank 6.96 0.01 6.90\n",
"MALIGCOUNT km 14.70 <0.005 12.96\n",
" rank 16.45 <0.005 14.29"
]
},
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"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"1. Variable 'AGE_DX' failed the non-proportional test: p-value is <5e-05.\n",
"\n",
" Advice 1: the functional form of the variable 'AGE_DX' might be incorrect. That is, there may be\n",
"non-linear terms missing. The proportional hazard test used is very sensitive to incorrect\n",
"functional forms. See documentation in link [D] below on how to specify a functional form.\n",
"\n",
" Advice 2: try binning the variable 'AGE_DX' using pd.cut, and then specify it in\n",
"`strata=['AGE_DX', ...]` in the call in `.fit`. See documentation in link [B] below.\n",
"\n",
" Advice 3: try adding an interaction term with your time variable. See documentation in link [C]\n",
"below.\n",
"\n",
"\n",
"2. Variable 'EOD10_SZ' failed the non-proportional test: p-value is 0.0083.\n",
"\n",
" Advice 1: the functional form of the variable 'EOD10_SZ' might be incorrect. That is, there may\n",
"be non-linear terms missing. The proportional hazard test used is very sensitive to incorrect\n",
"functional forms. See documentation in link [D] below on how to specify a functional form.\n",
"\n",
" Advice 2: try binning the variable 'EOD10_SZ' using pd.cut, and then specify it in\n",
"`strata=['EOD10_SZ', ...]` in the call in `.fit`. See documentation in link [B] below.\n",
"\n",
" Advice 3: try adding an interaction term with your time variable. See documentation in link [C]\n",
"below.\n",
"\n",
"\n",
"3. Variable 'MALIGCOUNT' failed the non-proportional test: p-value is <5e-05.\n",
"\n",
" Advice 1: the functional form of the variable 'MALIGCOUNT' might be incorrect. That is, there may\n",
"be non-linear terms missing. The proportional hazard test used is very sensitive to incorrect\n",
"functional forms. See documentation in link [D] below on how to specify a functional form.\n",
"\n",
" Advice 2: try binning the variable 'MALIGCOUNT' using pd.cut, and then specify it in\n",
"`strata=['MALIGCOUNT', ...]` in the call in `.fit`. See documentation in link [B] below.\n",
"\n",
" Advice 3: try adding an interaction term with your time variable. See documentation in link [C]\n",
"below.\n",
"\n",
"\n",
"---\n",
"[A] https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html\n",
"[B] https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Bin-variable-and-stratify-on-it\n",
"[C] https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Introduce-time-varying-covariates\n",
"[D] https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Modify-the-functional-form\n",
"[E] https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html#Stratification\n",
"\n"
]
},
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