{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(censored_data)=\n",
    "# Censored Data Models\n",
    "\n",
    ":::{post} May, 2022\n",
    ":tags: censored, survival analysis \n",
    ":category: intermediate, how-to\n",
    ":author: Luis Mario Domenzain\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import copy\n",
    "\n",
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pymc as pm\n",
    "\n",
    "from numpy.random import default_rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina'\n",
    "rng = default_rng(1234)\n",
    "az.style.use(\"arviz-variat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[This example notebook on Bayesian survival analysis](https://www.pymc.io/projects/examples/en/latest/survival_analysis/survival_analysis.html) touches on the point of censored data. _Censoring_ is a form of missing-data problem, in which observations greater than a certain threshold are clipped down to that threshold, or observations less than a certain threshold are clipped up to that threshold, or both. These are called right, left and interval censoring,respectively. In this example notebook we consider interval censoring.\n",
    "\n",
    "Censored data arises in many modelling problems. Two common examples are:\n",
    "\n",
    "1. _Survival analysis:_ when studying the effect of a certain medical treatment on survival times, it is impossible to prolong the study until all subjects have died. At the end of the study, the only data collected for many patients is that they were still alive for a time period $T$ after the treatment was administered: in reality, their true survival times are greater than $T$.\n",
    "\n",
    "2. _Sensor saturation:_ a sensor might have a limited range and the upper and lower limits would simply be the highest and lowest values a sensor can report. For instance, many mercury thermometers only report a very narrow range of temperatures.\n",
    "\n",
    "This example notebook presents two different ways of dealing with censored data\n",
    "in PyMC:\n",
    "\n",
    "1. An imputed censored model, which represents censored data as parameters and makes up plausible values for all censored values. As a result of this imputation, this model is capable of generating plausible sets of made-up values that would have been censored. Each censored element introduces a random variable.\n",
    "\n",
    "2. An unimputed censored model, where the censored data are integrated out and accounted for only through the log-likelihood. This method deals more adequately with large amounts of censored data and converges more quickly.\n",
    "\n",
    "To establish a baseline we compare to an uncensored model of the uncensored data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Produce normally distributed samples\n",
    "size = 500\n",
    "true_mu = 13.0\n",
    "true_sigma = 5.0\n",
    "samples = rng.normal(true_mu, true_sigma, size)\n",
    "\n",
    "# Set censoring limits\n",
    "low = 3.0\n",
    "high = 16.0\n",
    "\n",
    "\n",
    "def censor(x, low, high):\n",
    "    x = copy(x)\n",
    "    x[x <= low] = low\n",
    "    x[x >= high] = high\n",
    "    return x\n",
    "\n",
    "\n",
    "# Censor samples\n",
    "censored = censor(samples, low, high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 623,
       "width": 2023
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize uncensored and censored data\n",
    "_, ax = plt.subplots(figsize=(10, 3))\n",
    "edges = np.linspace(-5, 35, 30)\n",
    "ax.hist(samples, bins=edges, density=True, histtype=\"stepfilled\", alpha=0.4, label=\"Uncensored\")\n",
    "ax.hist(censored, bins=edges, density=True, histtype=\"stepfilled\", alpha=0.4, label=\"Censored\")\n",
    "[ax.axvline(x=x, c=\"k\", ls=\"--\") for x in [low, high]]\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uncensored Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def uncensored_model(data):\n",
    "    with pm.Model() as model:\n",
    "        mu = pm.Normal(\"mu\", mu=((high - low) / 2) + low, sigma=(high - low))\n",
    "        sigma = pm.HalfNormal(\"sigma\", sigma=(high - low) / 2.0)\n",
    "        observed = pm.Normal(\"observed\", mu=mu, sigma=sigma, observed=data)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should predict that running the uncensored model on uncensored data, we will get reasonable estimates of the mean and variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [mu, sigma]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fef3d926bbc346a796a027e00692efa3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 13 seconds.\n"
     ]
    }
   ],
   "source": [
    "uncensored_model_1 = uncensored_model(samples)\n",
    "with uncensored_model_1:\n",
    "    idata = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x535.887 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 559,
       "width": 2423
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = az.plot_dist(idata)\n",
    "az.add_lines(pc, {\"mu\": true_mu, \"sigma\": true_sigma});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And that is exactly what we find. \n",
    "\n",
    "The problem however, is that in censored data contexts, we do not have access to the true values. If we were to use the same uncensored model on the censored data, we would anticipate that our parameter estimates will be biased. If we calculate point estimates for the mean and standard deviation, then we can see that we are likely to underestimate the mean and standard deviation for this particular dataset and censor bounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean=12.32; std=3.76\n"
     ]
    }
   ],
   "source": [
    "print(f\"mean={np.mean(censored):.2f}; std={np.std(censored):.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [mu, sigma]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55e3820577e84461862911438a7d965e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 13 seconds.\n"
     ]
    }
   ],
   "source": [
    "uncensored_model_2 = uncensored_model(censored)\n",
    "with uncensored_model_2:\n",
    "    idata = pm.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, we see that both the mean and standard deviation are underestimated when using the uncensored model on censored data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x535.887 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 559,
       "width": 2423
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = az.plot_dist(idata)\n",
    "az.add_lines(pc, {\"mu\": true_mu, \"sigma\": true_sigma});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Censored data models\n",
    "\n",
    "The models below show two approaches to dealing with censored data. First, we need to do a bit of data pre-processing to count the number of observations that are left or right censored. We also need to extract just the non-censored data that we observe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(censored_data/model1)=\n",
    "### Model 1 - Imputed Censored Model of Censored Data\n",
    "\n",
    "In this model, we impute the censored values from the same distribution as the uncensored data. Sampling from the posterior generates possible uncensored data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_right_censored = sum(censored >= high)\n",
    "n_left_censored = sum(censored <= low)\n",
    "n_observed = len(censored) - n_right_censored - n_left_censored\n",
    "uncensored = censored[(censored > low) & (censored < high)]\n",
    "assert len(uncensored) == n_observed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [mu, sigma, right_censored, left_censored]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41bfa05169ac4c1ab43cb3a19606b51d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 15 seconds.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as imputed_censored_model:\n",
    "    mu = pm.Normal(\"mu\", mu=((high - low) / 2) + low, sigma=(high - low))\n",
    "    sigma = pm.HalfNormal(\"sigma\", sigma=(high - low) / 2.0)\n",
    "    right_censored = pm.Normal(\n",
    "        \"right_censored\",\n",
    "        mu,\n",
    "        sigma,\n",
    "        transform=pm.distributions.transforms.Interval(high, None),\n",
    "        shape=int(n_right_censored),\n",
    "        initval=np.full(n_right_censored, high + 1),\n",
    "    )\n",
    "    left_censored = pm.Normal(\n",
    "        \"left_censored\",\n",
    "        mu,\n",
    "        sigma,\n",
    "        transform=pm.distributions.transforms.Interval(None, low),\n",
    "        shape=int(n_left_censored),\n",
    "        initval=np.full(n_left_censored, low - 1),\n",
    "    )\n",
    "    observed = pm.Normal(\"observed\", mu=mu, sigma=sigma, observed=uncensored, shape=int(n_observed))\n",
    "    idata = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x535.887 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 559,
       "width": 2423
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = az.plot_dist(idata, var_names=[\"mu\", \"sigma\"])\n",
    "az.add_lines(pc, {\"mu\": true_mu, \"sigma\": true_sigma});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the bias in the estimates of the mean and standard deviation (present in the uncensored model) have been largely removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 2 - Unimputed Censored Model of Censored Data\n",
    "\n",
    "Here we can make use of `pm.Censored`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [mu, sigma]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b224dc3fe28e444d92deb5178daf590d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/osvaldo/anaconda3/envs/arviz_1/lib/python3.14/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot\n",
      "  return 0.5 * np.dot(x, v_out)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 14 seconds.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as unimputed_censored_model:\n",
    "    mu = pm.Normal(\"mu\", mu=0.0, sigma=(high - low) / 2.0)\n",
    "    sigma = pm.HalfNormal(\"sigma\", sigma=(high - low) / 2.0)\n",
    "    y_latent = pm.Normal.dist(mu=mu, sigma=sigma)\n",
    "    obs = pm.Censored(\"obs\", y_latent, lower=low, upper=high, observed=censored)\n",
    "    idata = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2400x535.887 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 559,
       "width": 2423
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = az.plot_dist(idata, var_names=[\"mu\", \"sigma\"])\n",
    "az.add_lines(pc, {\"mu\": true_mu, \"sigma\": true_sigma});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the bias in the estimates of the mean and variance (present in the uncensored model) have been largely removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "As we can see, both censored models appear to capture the mean and variance of the underlying distribution as well as the uncensored model! In addition, the imputed censored model is capable of generating data sets of censored values (sample from the posteriors of `left_censored` and `right_censored` to generate them), while the unimputed censored model scales much better with more censored data, and converges faster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n",
    "\n",
    "- Originally authored by [Luis Mario Domenzain](https://github.com/domenzain) on Mar 7, 2017.\n",
    "- Updated by [George Ho](https://github.com/eigenfoo) on Jul 14, 2018.\n",
    "- Updated by [Benjamin Vincent](https://github.com/drbenvincent) in May 2021.\n",
    "- Updated by [Benjamin Vincent](https://github.com/drbenvincent) in May 2022.\n",
    "- Updated by [Osvaldo Martin](https://github.com/aloctavodia) in Dec 2025.\n",
    "- Updated by [Osvaldo Martin](https://github.com/aloctavodia) in Apr 2026."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Sat, 25 Apr 2026\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.14.4\n",
      "IPython version      : 9.12.0\n",
      "\n",
      "pytensor: 2.38.0+133.g80cc113b5\n",
      "aeppl   : unknown\n",
      "\n",
      "arviz     : 1.1.0\n",
      "matplotlib: 3.10.8\n",
      "numpy     : 2.4.4\n",
      "pymc      : 5.28.0+58.gf58491a3\n",
      "\n",
      "Watermark: 2.6.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor,aeppl"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{include} ../page_footer.md\n",
    ":::"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "arviz_1",
   "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": "3.14.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}