{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:stancache.seed:Setting seed to 1245502385\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", "import random\n", "random.seed(1100038344)\n", "import survivalstan\n", "import numpy as np\n", "import pandas as pd\n", "from stancache import stancache\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The model\n", "\n", "This style of modeling is often called the \"piecewise exponential model\", or PEM. It is the simplest case where we estimate the *hazard* of an event occurring in a time period as the outcome, rather than estimating the *survival* (ie, time to event) as the outcome.\n", "\n", "Recall that, in the context of survival modeling, we have two models:\n", "\n", "1. A model for **Survival ($S$)**, ie the probability of surviving to time $t$:\n", "\n", " $$ S(t)=Pr(Y > t) $$\n", "\n", "2. A model for the **instantaneous *hazard* $\\lambda$**, ie the probability of a failure event occuring in the interval [$t$, $t+\\delta t$], given survival to time $t$:\n", "\n", " $$ \\lambda(t) = \\lim_{\\delta t \\rightarrow 0 } \\; \\frac{Pr( t \\le Y \\le t + \\delta t | Y > t)}{\\delta t} $$\n", "\n", "\n", "By definition, these two are related to one another by the following equation:\n", "\n", " $$ \\lambda(t) = \\frac{-S'(t)}{S(t)} $$\n", " \n", "Solving this, yields the following:\n", "\n", " $$ S(t) = \\exp\\left( -\\int_0^t \\lambda(z) dz \\right) $$\n", "\n", "This model is called the **piecewise exponential model** because of this relationship between the Survival and hazard functions. It's piecewise because we are not estimating the *instantaneous* hazard; we are instead breaking time periods up into pieces and estimating the hazard for each piece.\n", "\n", "There are several variations on the PEM model implemented in `survivalstan`. In this notebook, we are exploring just one of them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A note about data formatting \n", "\n", "When we model *Survival*, we typically operate on data in time-to-event form. In this form, we have one record per `Subject` (ie, per patient). Each record contains `[event_status, time_to_event]` as the outcome. This data format is sometimes called *per-subject*.\n", "\n", "When we model the *hazard* by comparison, we typically operate on data that are transformed to include one record per `Subject` per `time_period`. This is called *per-timepoint* or *long* form.\n", "\n", "All other things being equal, a model for *Survival* will typically estimate more efficiently (faster & smaller memory footprint) than one for *hazard* simply because the data are larger in the per-timepoint form than the per-subject form. The benefit of the *hazard* models is increased flexibility in terms of specifying the baseline hazard, time-varying effects, and introducing time-varying covariates.\n", "\n", "In this example, we are demonstrating use of the standard **PEM survival model**, which uses data in long form. The `stan` code expects to recieve data in this structure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stan code for the model\n", "\n", "This model is provided in `survivalstan.models.pem_survival_model`. Let's take a look at the stan code. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/* Variable naming:\n", " // dimensions\n", " N = total number of observations (length of data)\n", " S = number of sample ids\n", " T = max timepoint (number of timepoint ids)\n", " M = number of covariates\n", " \n", " // main data matrix (per observed timepoint*record)\n", " s = sample id for each obs\n", " t = timepoint id for each obs\n", " event = integer indicating if there was an event at time t for sample s\n", " x = matrix of real-valued covariates at time t for sample n [N, X]\n", " \n", " // timepoint-specific data (per timepoint, ordered by timepoint id)\n", " t_obs = observed time since origin for each timepoint id (end of period)\n", " t_dur = duration of each timepoint period (first diff of t_obs)\n", " \n", "*/\n", "// Jacqueline Buros Novik \n", "\n", "data {\n", " // dimensions\n", " int N;\n", " int S;\n", " int T;\n", " int M;\n", " \n", " // data matrix\n", " int s[N]; // sample id\n", " int t[N]; // timepoint id\n", " int event[N]; // 1: event, 0:censor\n", " matrix[N, M] x; // explanatory vars\n", " \n", " // timepoint data\n", " vector[T] t_obs;\n", " vector[T] t_dur;\n", "}\n", "transformed data {\n", " vector[T] log_t_dur; // log-duration for each timepoint\n", " int n_trans[S, T]; \n", " \n", " log_t_dur = log(t_dur);\n", "\n", " // n_trans used to map each sample*timepoint to n (used in gen quantities)\n", " // map each patient/timepoint combination to n values\n", " for (n in 1:N) {\n", " n_trans[s[n], t[n]] = n;\n", " }\n", "\n", " // fill in missing values with n for max t for that patient\n", " // ie assume \"last observed\" state applies forward (may be problematic for TVC)\n", " // this allows us to predict failure times >= observed survival times\n", " for (samp in 1:S) {\n", " int last_value;\n", " last_value = 0;\n", " for (tp in 1:T) {\n", " // manual says ints are initialized to neg values\n", " // so <=0 is a shorthand for \"unassigned\"\n", " if (n_trans[samp, tp] <= 0 && last_value != 0) {\n", " n_trans[samp, tp] = last_value;\n", " } else {\n", " last_value = n_trans[samp, tp];\n", " }\n", " }\n", " } \n", "}\n", "parameters {\n", " vector[T] log_baseline_raw; // unstructured baseline hazard for each timepoint t\n", " vector[M] beta; // beta for each covariate\n", " real baseline_sigma;\n", " real log_baseline_mu;\n", "}\n", "transformed parameters {\n", " vector[N] log_hazard;\n", " vector[T] log_baseline; // unstructured baseline hazard for each timepoint t\n", " \n", " log_baseline = log_baseline_mu + log_baseline_raw + log_t_dur;\n", " \n", " for (n in 1:N) {\n", " log_hazard[n] = log_baseline[t[n]] + x[n,]*beta;\n", " }\n", "}\n", "model {\n", " beta ~ cauchy(0, 2);\n", " event ~ poisson_log(log_hazard);\n", " log_baseline_mu ~ normal(0, 1);\n", " baseline_sigma ~ normal(0, 1);\n", " log_baseline_raw ~ normal(0, baseline_sigma);\n", "}\n", "generated quantities {\n", " real log_lik[N];\n", " vector[T] baseline;\n", " real y_hat_time[S]; // predicted failure time for each sample\n", " int y_hat_event[S]; // predicted event (0:censor, 1:event)\n", " \n", " // compute raw baseline hazard, for summary/plotting\n", " baseline = exp(log_baseline_mu + log_baseline_raw);\n", " \n", " // prepare log_lik for loo-psis\n", " for (n in 1:N) {\n", " log_lik[n] = poisson_log_log(event[n], log_hazard[n]);\n", " }\n", "\n", " // posterior predicted values\n", " for (samp in 1:S) {\n", " int sample_alive;\n", " sample_alive = 1;\n", " for (tp in 1:T) {\n", " if (sample_alive == 1) {\n", " int n;\n", " int pred_y;\n", " real log_haz;\n", " \n", " // determine predicted value of this sample's hazard\n", " n = n_trans[samp, tp];\n", " log_haz = log_baseline[tp] + x[n,] * beta;\n", " \n", " // now, make posterior prediction of an event at this tp\n", " if (log_haz < log(pow(2, 30))) \n", " pred_y = poisson_log_rng(log_haz);\n", " else\n", " pred_y = 9; \n", " \n", " // summarize survival time (observed) for this pt\n", " if (pred_y >= 1) {\n", " // mark this patient as ineligible for future tps\n", " // note: deliberately treat 9s as events \n", " sample_alive = 0;\n", " y_hat_time[samp] = t_obs[tp];\n", " y_hat_event[samp] = 1;\n", " }\n", " \n", " }\n", " } // end per-timepoint loop\n", " \n", " // if patient still alive at max\n", " if (sample_alive == 1) {\n", " y_hat_time[samp] = t_obs[T];\n", " y_hat_event[samp] = 0;\n", " }\n", " } // end per-sample loop \n", "}\n", "\n" ] } ], "source": [ "print(survivalstan.models.pem_survival_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulate survival data " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to demonstrate the use of this model, we will first simulate some survival data using `survivalstan.sim.sim_data_exp_correlated`. As the name implies, this function simulates data assuming a constant hazard throughout the follow-up time period, which is consistent with the Exponential survival function.\n", "\n", "This function includes two simulated covariates by default (`age` and `sex`). We also simulate a situation where hazard is a function of the simulated value for `sex`. \n", "\n", "We also center the `age` variable since this will make it easier to interpret estimates of the baseline hazard.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:stancache.stancache:sim_data_exp_correlated: cache_filename set to sim_data_exp_correlated.cached.N_100.censor_time_20.rate_coefs_54462717316.rate_form_1 + sex.pkl\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:stancache.stancache:sim_data_exp_correlated: Loading result from cache\n" ] } ], "source": [ "d = stancache.cached(\n", " survivalstan.sim.sim_data_exp_correlated,\n", " N=100,\n", " censor_time=20,\n", " rate_form='1 + sex',\n", " rate_coefs=[-3, 0.5],\n", ")\n", "d['age_centered'] = d['age'] - d['age'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Aside: In order to make this a more reproducible example, this code is using a file-caching function `stancache.cached` to wrap a function call to `survivalstan.sim.sim_data_exp_correlated`. *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore simulated data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is what these data look like - this is `per-subject` or `time-to-event` form:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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sexageratetrue_tteventindexage_centered
0male540.0820851.0138551.013855True0-1.12
1male390.0820854.8905974.890597True1-16.12
2female450.0497874.0934044.093404True2-10.12
3female430.0497877.0362267.036226True3-12.12
4female570.0497875.7122995.712299True41.88
\n", "
" ], "text/plain": [ " sex age rate true_t t event index age_centered\n", "0 male 54 0.082085 1.013855 1.013855 True 0 -1.12\n", "1 male 39 0.082085 4.890597 4.890597 True 1 -16.12\n", "2 female 45 0.049787 4.093404 4.093404 True 2 -10.12\n", "3 female 43 0.049787 7.036226 7.036226 True 3 -12.12\n", "4 female 57 0.049787 5.712299 5.712299 True 4 1.88" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*It's not that obvious from the field names, but in this example \"subjects\" are indexed by the field `index`.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot these data using `lifelines`, or the rudimentary plotting functions provided by `survivalstan`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "survivalstan.utils.plot_observed_survival(df=d[d['sex']=='female'], event_col='event', time_col='t', label='female')\n", "survivalstan.utils.plot_observed_survival(df=d[d['sex']=='male'], event_col='event', time_col='t', label='male')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transform to `long` or `per-timepoint` form" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, since this is a PEM model, we transform our data to `long` or `per-timepoint` form." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:stancache.stancache:prep_data_long_surv: cache_filename set to prep_data_long_surv.cached.df_14209590808.event_col_event.time_col_t.pkl\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:stancache.stancache:prep_data_long_surv: Loading result from cache\n" ] } ], "source": [ "dlong = stancache.cached(\n", " survivalstan.prep_data_long_surv,\n", " df=d, event_col='event', time_col='t'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have one record per timepoint (distinct values of `end_time`) per subject (`index`, in the original data frame)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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148male390.0820854.8905974.890597True1-16.120.009787False
140male390.0820854.8905974.890597True1-16.120.377535False
147male390.0820854.8905974.890597True1-16.120.791192False
133male390.0820854.8905974.890597True1-16.120.808987False
75male390.0820854.8905974.890597True1-16.121.013855False
128male390.0820854.8905974.890597True1-16.121.052508False
106male390.0820854.8905974.890597True1-16.121.467963False
102male390.0820854.8905974.890597True1-16.121.517398False
118male390.0820854.8905974.890597True1-16.121.653389False
123male390.0820854.8905974.890597True1-16.121.684769False
119male390.0820854.8905974.890597True1-16.121.713038False
104male390.0820854.8905974.890597True1-16.122.125944False
103male390.0820854.8905974.890597True1-16.122.558112False
135male390.0820854.8905974.890597True1-16.122.656621False
92male390.0820854.8905974.890597True1-16.122.692360False
138male390.0820854.8905974.890597True1-16.122.701946False
95male390.0820854.8905974.890597True1-16.122.829331False
87male390.0820854.8905974.890597True1-16.122.942428False
108male390.0820854.8905974.890597True1-16.123.025977False
125male390.0820854.8905974.890597True1-16.123.034875False
93male390.0820854.8905974.890597True1-16.123.095593False
129male390.0820854.8905974.890597True1-16.123.111741False
139male390.0820854.8905974.890597True1-16.123.641794False
117male390.0820854.8905974.890597True1-16.123.771836False
83male390.0820854.8905974.890597True1-16.124.092917False
77male390.0820854.8905974.890597True1-16.124.093404False
145male390.0820854.8905974.890597True1-16.124.200436False
126male390.0820854.8905974.890597True1-16.124.451497False
89male390.0820854.8905974.890597True1-16.124.591072False
81male390.0820854.8905974.890597True1-16.124.694461False
76male390.0820854.8905974.890597True1-16.124.890597True
\n", "
" ], "text/plain": [ " sex age rate true_t t event index age_centered \\\n", "148 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "140 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "147 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "133 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "75 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "128 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "106 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "102 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "118 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "123 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "119 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "104 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "103 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "135 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "92 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "138 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "95 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "87 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "108 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "125 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "93 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "129 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "139 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "117 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "83 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "77 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "145 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "126 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "89 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "81 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "76 male 39 0.082085 4.890597 4.890597 True 1 -16.12 \n", "\n", " end_time end_failure \n", "148 0.009787 False \n", "140 0.377535 False \n", "147 0.791192 False \n", "133 0.808987 False \n", "75 1.013855 False \n", "128 1.052508 False \n", "106 1.467963 False \n", "102 1.517398 False \n", "118 1.653389 False \n", "123 1.684769 False \n", "119 1.713038 False \n", "104 2.125944 False \n", "103 2.558112 False \n", "135 2.656621 False \n", "92 2.692360 False \n", "138 2.701946 False \n", "95 2.829331 False \n", "87 2.942428 False \n", "108 3.025977 False \n", "125 3.034875 False \n", "93 3.095593 False \n", "129 3.111741 False \n", "139 3.641794 False \n", "117 3.771836 False \n", "83 4.092917 False \n", "77 4.093404 False \n", "145 4.200436 False \n", "126 4.451497 False \n", "89 4.591072 False \n", "81 4.694461 False \n", "76 4.890597 True " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dlong.query('index == 1').sort_values('end_time')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit stan model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we are ready to fit our model using `survivalstan.fit_stan_survival_model`. \n", "\n", "We pass a few parameters to the fit function, many of which are required. See ?survivalstan.fit_stan_survival_model for details. \n", "\n", "Similar to what we did above, we are asking `survivalstan` to cache this model fit object. See [stancache](http://github.com/jburos/stancache) for more details on how this works. Also, if you didn't want to use the cache, you could omit the parameter `FIT_FUN` and `survivalstan` would use the standard pystan functionality.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jacki/projects/survivalstan/survivalstan/survivalstan.py:368: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", " 'x': self.x_df.as_matrix(),\n", "INFO:stancache.stancache:Step 1: Get compiled model code, possibly from cache\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:stancache.stancache:StanModel: cache_filename set to anon_model.cython_0_29_2.model_code_6711018461227478976.pystan_2_18_1_0.stanmodel.pkl\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:stancache.stancache:StanModel: Loading result from cache\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:stancache.stancache:Step 2: Get posterior draws from model, possibly from cache\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:stancache.stancache:sampling: cache_filename set to anon_model.cython_0_29_2.model_code_6711018461227478976.pystan_2_18_1_0.stanfit.chains_4.data_75284643319.iter_5000.seed_9001.pkl\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:stancache.stancache:sampling: Loading result from cache\n" ] } ], "source": [ "testfit = survivalstan.fit_stan_survival_model(\n", " model_cohort = 'test model',\n", " model_code = survivalstan.models.pem_survival_model,\n", " df = dlong,\n", " sample_col = 'index',\n", " timepoint_end_col = 'end_time',\n", " event_col = 'end_failure',\n", " formula = '~ age_centered + sex',\n", " iter = 5000,\n", " chains = 4,\n", " seed = 9001,\n", " FIT_FUN = stancache.cached_stan_fit,\n", " )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Superficial review of convergence\n", "\n", "We will note here some top-level summaries of posterior draws -- this is a minimal example so it's unlikely that this model converged very well. \n", "\n", "In practice, you would want to do a lot more investigation of convergence issues, etc. For now the goal is to demonstrate the functionalities available here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can summarize posterior estimates for a single parameter, (e.g. the built-in Stan parameter `lp__`):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " mean se_mean sd 2.5% 50% 97.5% Rhat\n", "lp__ -343.716157 29.967469 84.346802 -428.904008 -371.820577 -101.853053 1.943707\n" ] } ], "source": [ "survivalstan.utils.print_stan_summary([testfit], pars='lp__')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or, for sets of parameters with the same name:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " mean se_mean sd 2.5% 50% 97.5% Rhat\n", "log_baseline_raw[1] 0.127908 0.011206 0.390646 -0.574587 0.045060 1.112140 1.009859\n", "log_baseline_raw[2] -0.180508 0.023764 0.347081 -1.042441 -0.093197 0.359572 1.024901\n", "log_baseline_raw[3] -0.204398 0.026817 0.353415 -1.078168 -0.110903 0.339344 1.031154\n", "log_baseline_raw[4] 0.111204 0.010062 0.374736 -0.587863 0.032338 1.035699 1.008499\n", "log_baseline_raw[5] -0.076325 0.005433 0.334775 -0.865588 -0.019305 0.555003 1.004544\n", "log_baseline_raw[6] 0.088543 0.005141 0.366412 -0.627267 0.028007 0.930208 1.004422\n", "log_baseline_raw[7] -0.192198 0.026126 0.345796 -1.023044 -0.100468 0.334006 1.028990\n", "log_baseline_raw[8] 0.077545 0.005527 0.362746 -0.625327 0.021046 0.914410 1.003724\n", "log_baseline_raw[9] -0.007915 0.002938 0.340064 -0.792517 0.000030 0.720585 0.999816\n", "log_baseline_raw[10] 0.102421 0.008460 0.372675 -0.585478 0.031353 1.009513 1.006293\n", "log_baseline_raw[11] 0.105546 0.008672 0.375497 -0.595675 0.028599 1.018843 1.007367\n", "log_baseline_raw[12] -0.179333 0.024334 0.341492 -1.029340 -0.095580 0.361123 1.024614\n", "log_baseline_raw[13] -0.189801 0.025664 0.351737 -1.063312 -0.098743 0.337103 1.027370\n", "log_baseline_raw[14] 0.030662 0.002810 0.348627 -0.716552 0.007330 0.795914 1.000677\n", "log_baseline_raw[15] 0.099584 0.007343 0.363191 -0.583793 0.027420 0.982254 1.006853\n", "log_baseline_raw[16] 0.127511 0.013778 0.396442 -0.575169 0.040235 1.085049 1.010791\n", "log_baseline_raw[17] 0.008104 0.002762 0.334577 -0.717536 0.001003 0.718622 0.999951\n", "log_baseline_raw[18] 0.017583 0.002839 0.348238 -0.739866 0.004059 0.786641 1.000018\n", "log_baseline_raw[19] 0.051770 0.003351 0.346987 -0.643967 0.012692 0.845073 1.001971\n", "log_baseline_raw[20] 0.129739 0.012444 0.392641 -0.551537 0.043589 1.088190 1.010026\n", "log_baseline_raw[21] 0.070802 0.003980 0.362372 -0.631452 0.017359 0.915291 1.003119\n", "log_baseline_raw[22] 0.120558 0.011412 0.401495 -0.612580 0.037298 1.107091 1.008950\n", "log_baseline_raw[23] -0.200919 0.026878 0.348461 -1.059523 -0.105612 0.319076 1.031085\n", "log_baseline_raw[24] 0.011504 0.002747 0.333063 -0.706409 0.002467 0.729663 0.999909\n", "log_baseline_raw[25] -0.100401 0.008833 0.342553 -0.927089 -0.029254 0.510422 1.007842\n", "log_baseline_raw[26] 0.145832 0.013621 0.409583 -0.548009 0.052751 1.148941 1.012278\n", "log_baseline_raw[27] 0.036857 0.003076 0.347189 -0.713045 0.009555 0.804570 1.000600\n", "log_baseline_raw[28] -0.050060 0.003738 0.332868 -0.821535 -0.009052 0.584618 1.002173\n", "log_baseline_raw[29] 0.013071 0.002745 0.343440 -0.723283 0.001292 0.748862 0.999932\n", "log_baseline_raw[30] 0.043152 0.002941 0.357395 -0.685283 0.009026 0.833400 1.000904\n", "log_baseline_raw[31] -0.017653 0.002685 0.326895 -0.761066 -0.002277 0.657733 0.999965\n", "log_baseline_raw[32] -0.011146 0.002848 0.338459 -0.777028 -0.001224 0.691604 0.999987\n", "log_baseline_raw[33] -0.081954 0.006583 0.333594 -0.883221 -0.020265 0.535545 1.005413\n", "log_baseline_raw[34] -0.036392 0.003265 0.329612 -0.797439 -0.006231 0.625224 1.000900\n", "log_baseline_raw[35] 0.083989 0.005369 0.368764 -0.641116 0.024087 0.950388 1.004270\n", "log_baseline_raw[36] 0.121632 0.011415 0.385119 -0.553989 0.042020 1.075079 1.008250\n", "log_baseline_raw[37] -0.159254 0.019448 0.336448 -1.001608 -0.074673 0.368831 1.020632\n", "log_baseline_raw[38] 0.113074 0.010449 0.376970 -0.574158 0.037438 1.059475 1.008356\n", "log_baseline_raw[39] 0.032572 0.002982 0.358596 -0.729394 0.006928 0.822086 1.000807\n", "log_baseline_raw[40] -0.055043 0.004043 0.325454 -0.800246 -0.011163 0.576579 1.002306\n", "log_baseline_raw[41] -0.061538 0.004504 0.332419 -0.848531 -0.010646 0.567399 1.002538\n", "log_baseline_raw[42] 0.018537 0.002863 0.344790 -0.722493 0.004048 0.762626 0.999984\n", "log_baseline_raw[43] 0.111469 0.008624 0.385652 -0.614831 0.034014 1.066727 1.007120\n", "log_baseline_raw[44] -0.349024 0.055005 0.388294 -1.290931 -0.256493 0.149242 1.077350\n", "log_baseline_raw[45] 0.036941 0.003198 0.348631 -0.681573 0.009248 0.808613 1.001145\n", "log_baseline_raw[46] 0.122952 0.011257 0.391445 -0.574266 0.039055 1.060185 1.008966\n", "log_baseline_raw[47] 0.066934 0.004777 0.367302 -0.659741 0.016456 0.916417 1.003241\n", "log_baseline_raw[48] 0.037662 0.002894 0.339226 -0.679402 0.007427 0.806976 1.000927\n", "log_baseline_raw[49] -0.128401 0.011939 0.335784 -0.942981 -0.049412 0.446420 1.013169\n", "log_baseline_raw[50] -0.055320 0.004402 0.336685 -0.840916 -0.009793 0.588261 1.002597\n", "log_baseline_raw[51] -0.165098 0.020264 0.340715 -1.012617 -0.080189 0.391560 1.022408\n", "log_baseline_raw[52] 0.092579 0.006717 0.370705 -0.615379 0.025183 0.976427 1.005714\n", "log_baseline_raw[53] 0.045766 0.003043 0.351404 -0.703664 0.012100 0.817258 1.001124\n", "log_baseline_raw[54] 0.026354 0.003112 0.344165 -0.711943 0.006029 0.780290 1.000427\n", "log_baseline_raw[55] -0.071239 0.004947 0.324377 -0.836377 -0.018402 0.548979 1.004454\n", "log_baseline_raw[56] -0.046464 0.003635 0.329123 -0.799863 -0.007735 0.591899 1.001597\n", "log_baseline_raw[57] 0.014518 0.002704 0.330862 -0.686842 0.003498 0.726152 1.000012\n", "log_baseline_raw[58] -0.085550 0.006782 0.332717 -0.889072 -0.026227 0.513838 1.006755\n", "log_baseline_raw[59] -0.265427 0.038779 0.366346 -1.158705 -0.168329 0.241646 1.048854\n", "log_baseline_raw[60] 0.009246 0.002874 0.348016 -0.762312 0.002954 0.751171 0.999709\n", "log_baseline_raw[61] -0.047970 0.003617 0.328642 -0.812721 -0.008988 0.596939 1.001983\n", "log_baseline_raw[62] -0.034102 0.003274 0.338180 -0.822991 -0.005110 0.650844 1.000867\n", "log_baseline_raw[63] 0.086715 0.006781 0.375946 -0.623444 0.022383 0.968243 1.004879\n", "log_baseline_raw[64] 0.119084 0.010080 0.388406 -0.582728 0.036834 1.049734 1.008377\n", "log_baseline_raw[65] 0.020136 0.002904 0.347704 -0.729227 0.005366 0.773813 1.000058\n", "log_baseline_raw[66] 0.012625 0.002687 0.337372 -0.712615 0.003221 0.729694 0.999990\n", "log_baseline_raw[67] -0.310176 0.046909 0.382413 -1.237388 -0.211220 0.198496 1.062100\n", "log_baseline_raw[68] 0.034832 0.003038 0.352946 -0.716226 0.007045 0.844364 1.000556\n", "log_baseline_raw[69] 0.029941 0.002712 0.346389 -0.706197 0.006358 0.781630 1.000219\n", "log_baseline_raw[70] -0.126101 0.013032 0.337670 -0.955255 -0.048448 0.460451 1.012497\n", "log_baseline_raw[71] 0.112147 0.009344 0.374716 -0.573337 0.036359 1.004377 1.007726\n", "log_baseline_raw[72] 0.055285 0.003619 0.355362 -0.657008 0.011551 0.863396 1.001964\n", "log_baseline_raw[73] 0.120600 0.009427 0.386573 -0.576560 0.037674 1.075977 1.008553\n", "log_baseline_raw[74] 0.119699 0.010666 0.391956 -0.601395 0.037771 1.085140 1.008365\n", "log_baseline_raw[75] -0.053538 0.004350 0.370626 -0.922223 -0.008979 0.685980 1.002351\n" ] } ], "source": [ "survivalstan.utils.print_stan_summary([testfit], pars='log_baseline_raw')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's also not uncommon to graphically summarize the `Rhat` values, to get a sense of similarity among the chains for particular parameters. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "survivalstan.utils.plot_stan_summary([testfit], pars='log_baseline_raw')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot posterior estimates of parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use `plot_coefs` to summarize posterior estimates of parameters. \n", "\n", "In this basic `pem_survival_model`, we estimate a parameter for baseline hazard for each observed timepoint which is then adjusted for the duration of the timepoint. For consistency, the baseline values are normalized to the *unit time* given in the input data. This allows us to compare hazard estimates across timepoints without having to know the duration of a timepoint. *(in general, the duration-adjusted hazard paramters are suffixed with `_raw` whereas those which are unit-normalized do not have a suffix).*\n", "\n", "In this model, the baseline hazard is parameterized by two components -- there is an overall mean across all timepoints (`log_baseline_mu`) and some variance per timepoint (`log_baseline_tp`). The degree of variance is estimated from the data as `log_baseline_sigma`. All components have weak default priors. See the stan code above for details.\n", "\n", "In this case, the model estimates a minimal degree of variance across timepoints, which is good given that the simulated data assumed a constant hazard over time. \n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "survivalstan.utils.plot_coefs([testfit], element='baseline')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also summarize the posterior estimates for our `beta` coefficients. This is actually the default behavior of `plot_coefs`. Here we hope to see the posterior estimates of beta coefficients include the value we used for our simulation (0.5)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "survivalstan.utils.plot_coefs([testfit])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Posterior predictive checking\n", "\n", "Finally, `survivalstan` provides some utilities for posterior predictive checking.\n", "\n", "The goal of posterior-predictive checking is to compare the uncertainty of model predictions to observed values.\n", "\n", "We are not doing *true* out-of-sample predictions, but we are able to sanity-check our model's calibration. We expect approximately 5% of observed values to fall outside of their corresponding 95% posterior-predicted intervals.\n", "\n", "By default, `survivalstan`'s plot_pp_survival method will plot whiskers at the 2.5th and 97.5th percentile values, corresponding to 95% predicted intervals." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "survivalstan.utils.plot_pp_survival([testfit], fill=False)\n", "survivalstan.utils.plot_observed_survival(df=d, event_col='event', time_col='t', color='green', label='observed')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also summarize and plot survival by our covariates of interest, provided they are included in the original dataframe provided to `fit_stan_survival_model`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "survivalstan.utils.plot_pp_survival([testfit], by='sex')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This plot can also be customized by a variety of aesthetic elements" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "survivalstan.utils.plot_pp_survival([testfit], by='sex', pal=['red', 'blue'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building up the plot semi-manually, for more customization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also access the utility methods within `survivalstan.utils` to more or less produce the same plot. This sequence is intended to both illustrate how the above-described plot was constructed, and expose some of the \n", "functionality in a more concrete fashion.\n", "\n", "Probably the most useful element is being able to summarize & return posterior-predicted values to begin with:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ppsurv = survivalstan.utils.prep_pp_survival_data([testfit], by='sex')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are what these data look like:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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itermodel_cohortsexlevel_3event_timesurvival
00test modelfemale00.0000001.000000
10test modelfemale10.7911921.000000
20test modelfemale21.4679630.975762
30test modelfemale32.1259440.920641
40test modelfemale43.6417940.911259
\n", "
" ], "text/plain": [ " iter model_cohort sex level_3 event_time survival\n", "0 0 test model female 0 0.000000 1.000000\n", "1 0 test model female 1 0.791192 1.000000\n", "2 0 test model female 2 1.467963 0.975762\n", "3 0 test model female 3 2.125944 0.920641\n", "4 0 test model female 4 3.641794 0.911259" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ppsurv.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(Note that this itself is a summary of the posterior draws returned by `survivalstan.utils.prep_pp_data`. In this case, the survival stats are summarized by values of `['iter', 'model_cohort', by]`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then call out to `survivalstan.utils._plot_pp_survival_data` to construct the plot. In this case, we overlay the posterior predicted intervals with observed values." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "subplot = plt.subplots(1, 1)\n", "survivalstan.utils._plot_pp_survival_data(ppsurv.query('sex == \"male\"').copy(),\n", " subplot=subplot, color='blue', alpha=0.5)\n", "survivalstan.utils._plot_pp_survival_data(ppsurv.query('sex == \"female\"').copy(),\n", " subplot=subplot, color='red', alpha=0.5)\n", "survivalstan.utils.plot_observed_survival(df=d[d['sex']=='female'], event_col='event', time_col='t',\n", " color='red', label='female')\n", "survivalstan.utils.plot_observed_survival(df=d[d['sex']=='male'], event_col='event', time_col='t',\n", " color='blue', label='male')\n", "plt.legend()" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }