{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Custom regression models\n", "\n", "Like for univariate models, it is possible to create your own custom parametric survival models. Why might you want to do this? \n", "\n", " - Create new / extend AFT models using known probability distributions\n", " - Create a piecewise model using domain knowledge about subjects\n", " - Iterate and fit a more accurate parametric model\n", "\n", "*lifelines* has a very simple API to create custom parametric regression models. The author only needs to define the cumulative hazard function. For example, the cumulative hazard for the Exponential regression model looks like:\n", "\n", "$$ \n", "H(t, x) = \\frac{t}{\\lambda(x)}\\\\ \\lambda(x) = \\exp{(\\vec{\\beta} \\cdot \\vec{x}^{\\,T})}\n", "$$ \n", "\n", "\n", "\n", "Below are some example custom models." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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modellifelines.ExponentialAFTFitter
duration col'week'
event col'arrest'
number of observations432
number of events observed114
log-likelihood-686.37
time fit was run2019-11-17 14:10:19 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%zp-log2(p)
lambda_fin0.371.440.19-0.010.740.992.101.920.064.18
age0.061.060.020.010.101.011.102.550.016.52
race-0.300.740.31-0.910.300.401.35-0.990.321.63
wexp0.151.160.21-0.270.560.761.750.690.491.03
mar0.431.530.38-0.321.170.733.241.120.261.93
paro0.081.090.20-0.300.470.741.590.420.670.57
prio-0.090.920.03-0.14-0.030.870.97-3.03<0.0058.65
intercept4.0557.440.592.905.2018.21181.156.91<0.00537.61
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from lifelines.fitters import ParametricRegressionFitter\n", "from autograd import numpy as np\n", "from lifelines.datasets import load_rossi\n", "\n", "\n", "class ExponentialAFTFitter(ParametricRegressionFitter):\n", " \n", " # this is necessary, and should always be a non-empty list of strings. \n", " _fitted_parameter_names = ['lambda_']\n", " \n", " def _cumulative_hazard(self, params, T, Xs):\n", " # params is a dictionary that maps unknown parameters to a numpy vector. \n", " # Xs is a dictionary that maps unknown parameters to a numpy 2d array \n", " lambda_ = np.exp(np.dot(Xs['lambda_'], params['lambda_']))\n", " return T / lambda_\n", " \n", "\n", "rossi = load_rossi()\n", "rossi['intercept'] = 1.0\n", "\n", "# the below variables maps dataframe columns to parameters\n", "regressors = {\n", " 'lambda_': rossi.columns\n", "}\n", "\n", "eaf = ExponentialAFTFitter().fit(rossi, 'week', 'arrest', regressors=regressors)\n", "eaf.print_summary()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/camerondavidson-pilon/code/lifelines/lifelines/fitters/__init__.py:1568: StatisticalWarning: The diagonal of the variance_matrix_ has negative values. This could be a problem with PolynomialCumulativeHazard's fit to the data.\n", "\n", "It's advisable to not trust the variances reported, and to be suspicious of the\n", "fitted parameters too.\n", "\n", " warnings.warn(warning_text, utils.StatisticalWarning)\n" ] }, { "data": { "text/html": [ "
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modellifelines.PolynomialCumulativeHazard
duration col'week'
event col'arrest'
penalizer0.5
number of observations432
number of events observed114
log-likelihood-680.15
time fit was run2019-11-17 14:10:19 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%zp-log2(p)
lambda1_fin0.251.280.18-0.100.600.911.821.400.162.64
age0.041.050.020.010.081.011.092.290.025.49
race-0.300.740.28-0.850.260.431.30-1.040.301.75
wexp0.151.160.19-0.230.520.801.680.770.441.18
mar0.301.350.31-0.300.910.742.470.980.331.61
paro0.061.060.18-0.300.420.741.520.310.760.40
prio-0.060.940.03-0.12-0.010.880.99-2.150.034.99
intercept4.70110.300.593.555.8634.73350.367.98<0.00549.23
lambda2_fin0.221.250.24-0.240.680.781.980.930.351.50
age0.051.050.020.000.101.001.102.080.044.75
race-0.150.860.36-0.850.550.431.74-0.410.680.56
wexp0.001.000.25-0.480.480.621.620.010.990.01
mar0.261.290.40-0.521.040.592.830.650.520.95
paro0.071.070.24-0.410.540.671.720.270.780.35
prio-0.060.940.04-0.140.020.871.02-1.460.142.79
intercept58.151.80e+25nannannannannannannannan
lambda3_fin0.191.210.14-0.090.470.911.601.330.182.44
age0.061.060.020.020.101.021.102.750.017.40
race0.001.000.19-0.370.370.691.450.010.990.01
wexp-0.140.870.15-0.440.160.641.17-0.930.351.52
mar0.221.240.30-0.380.810.682.250.710.481.06
paro0.081.080.14-0.190.340.821.410.550.580.78
prio-0.050.950.02-0.10-0.010.910.99-2.240.035.31
intercept3.4932.750.472.574.4113.0382.307.42<0.00542.96
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/camerondavidson-pilon/code/lifelines/lifelines/fitters/__init__.py:1568: StatisticalWarning: The diagonal of the variance_matrix_ has negative values. This could be a problem with PolynomialCumulativeHazard's fit to the data.\n", "\n", "It's advisable to not trust the variances reported, and to be suspicious of the\n", "fitted parameters too.\n", "\n", " warnings.warn(warning_text, utils.StatisticalWarning)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 261, "width": 424 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "from lifelines.fitters import ParametricRegressionFitter\n", "from autograd import numpy as np\n", "\n", "class PolynomialCumulativeHazard(ParametricRegressionFitter):\n", " \n", " _fitted_parameter_names = ['lambda1_', 'lambda2_', 'lambda3_']\n", " \n", " def _cumulative_hazard(self, params, T, Xs):\n", " lambda1_ = np.exp(np.dot(Xs['lambda1_'], params['lambda1_']))\n", " lambda2_ = np.exp(np.dot(Xs['lambda2_'], params['lambda2_']))\n", " lambda3_ = np.exp(np.dot(Xs['lambda3_'], params['lambda3_']))\n", "\n", " return (T/lambda1_) + (T/lambda2_)**2 + (T/lambda3_)**3\n", " \n", " def _add_penalty(self, params, neg_ll):\n", " # authors can create their own non-traditional penalty functions too. \n", " # This penalty is an \"information-pooling\" penalty, see more about it here:\n", " # https://dataorigami.net/blogs/napkin-folding/churn\n", " params_stacked = np.stack(params.values())\n", " coef_penalty = 0\n", "\n", " if self.penalizer > 0:\n", " for i in range(params_stacked.shape[1] - 1): # assuming the intercept col is the last column...\n", " coef_penalty = coef_penalty + (params_stacked[:, i]).var()\n", "\n", " return neg_ll + self.penalizer * coef_penalty\n", "\n", "rossi = load_rossi()\n", "rossi['intercept'] = 1.0\n", "\n", "# the below variables maps dataframe columns to parameters\n", "regressors = {\n", " 'lambda1_': rossi.columns,\n", " 'lambda2_': rossi.columns,\n", " 'lambda3_': rossi.columns\n", "}\n", "\n", "pf = PolynomialCumulativeHazard(penalizer=.5).fit(rossi, 'week', 'arrest', regressors=regressors)\n", "pf.print_summary()\n", "\n", "ax = plt.subplot()\n", "pf.plot(columns=['fin'], ax=ax)\n", "\n", "pf = PolynomialCumulativeHazard(penalizer=5.).fit(rossi, 'week', 'arrest', regressors=regressors)\n", "pf.plot(columns=['fin'], ax=ax, c=\"r\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cure models\n", "\n", "Suppose in our population we have a subpopulation that will never experience the event of interest. Or, for some subjects the event will occur so far in the future that it's essentially at time infinity. In this case, the survival function for an individual should not asymptically approach zero, but _some positive value_. Models that describe this are sometimes called cure models (i.e. the subject is \"cured\" of death and hence no longer susceptible) or time-lagged conversion models. \n", "\n", "It would be nice to be able to use common survival models _and_ have some \"cure\" component. Let's suppose that for individuals that will experience the event of interest, their survival distrubtion is a Weibull, denoted $S_W(t)$. For a random selected individual in the population, thier survival curve, $S(t)$, is:\n", "\n", "$$ \n", "\\begin{align*}\n", "S(t) = P(T > t) &= P(\\text{cured}) P(T > t\\;|\\;\\text{cured}) + P(\\text{not cured}) P(T > t\\;|\\;\\text{not cured}) \\\\\n", " &= p + (1-p) S_W(t)\n", "\\end{align*}\n", "$$\n", "\n", "Even though it's in an unconvential form, we can still determine the cumulative hazard (which is the negative logarithm of the survival function):\n", "\n", "$$ H(t) = -\\log{\\left(p + (1-p) S_W(t)\\right)} $$" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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modellifelines.CureModel
duration col'week'
event col'arrest'
penalizer0.1
number of observations432
number of events observed114
log-likelihood-729.43
time fit was run2019-11-17 14:15:11 UTC
\n", "
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%zp-log2(p)
lambda_fin-0.010.990.11-0.240.210.791.23-0.130.900.15
age0.021.020.02-0.010.050.991.051.110.271.90
race-0.110.890.27-0.640.410.531.50-0.430.670.59
wexp-0.060.940.21-0.470.350.631.42-0.270.780.35
mar0.571.770.210.170.981.182.672.760.017.42
paro1.303.660.121.071.532.914.6011.06<0.00592.09
prio0.081.080.020.040.121.041.123.94<0.00513.59
intercept0.391.480.100.200.591.221.803.99<0.00513.90
rho_intercept0.191.210.10-0.010.400.991.491.860.063.99
beta_age0.011.010.01-0.010.040.991.041.110.271.91
wexp0.131.140.13-0.120.380.891.461.020.311.71
paro0.551.740.220.130.981.142.652.570.016.60
intercept0.231.260.080.070.391.071.482.81<0.0057.65
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from autograd.scipy.special import expit\n", "\n", "class CureModel(ParametricRegressionFitter):\n", "\n", " _fitted_parameter_names = [\"lambda_\", \"rho_\", \"beta_\"]\n", "\n", " def _cumulative_hazard(self, params, T, Xs):\n", " c = expit(np.dot(Xs[\"beta_\"], params[\"beta_\"]))\n", "\n", " lambda_ = np.exp(np.dot(Xs[\"lambda_\"], params[\"lambda_\"]))\n", " rho_ = np.exp(np.dot(Xs[\"rho_\"], params[\"rho_\"]))\n", " sf = np.exp(-(T / lambda_) ** rho_)\n", "\n", " return -np.log((1-c) + c * sf)\n", "\n", "\n", "swf = CureModel(penalizer=.1)\n", "\n", "rossi = load_rossi()\n", "rossi[\"intercept\"] = 1.0\n", "\n", "covariates = {\"lambda_\": rossi.columns, \"rho_\": [\"intercept\"], \"beta_\": rossi.columns[1::2]}\n", "\n", "swf.fit(rossi, \"week\", event_col=\"arrest\", regressors=covariates, timeline=np.arange(250))\n", "swf.print_summary(2)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "image/png": { "height": 357, "width": 706 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# what's the effect on the survival curve if I vary \"age\"\n", "fig, ax = plt.subplots(figsize=(12, 6))\n", "\n", "swf.plot_covariate_groups(['age'], values=np.arange(20, 50, 5), cmap='coolwarm', ax=ax)" ] }, { "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }