Codes/19_BioassayExample.ipynb

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   "source": [
    "import numpy as np\n",
    "\n",
    "import pymc as pm\n",
    "import arviz as az\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arviz.labels as azl\n",
    "\n",
    "labeller = azl.MapLabeller(var_name_map = {\"Alpha\": r\"$\\alpha$\", \"Beta\": r\"$\\beta$\"})"
   ]
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   "cell_type": "markdown",
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   "source": [
    "# Analysis of a bioassay experiment"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 3.7 from BDA3"
   ]
  },
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   "source": [
    "In the development of drugs and other chemical compounds, acute toxicity tests or bioassay experiments are commonly performed on animals. Such experiments proceed by administering various dose levels of the compound to batches of animals. The animals’ responses are typically characterized by a dichotomous outcome: for example, alive or dead, tumor or no tumor. An experiment of this kind gives rise to data of the form $$(X_i,n_i,Y_i);\\quad i=1,\\ldots,k,$$ where $X_i$ represents the $i$ th of $k$ dose levels (often measured on a logarithmic scale) given to $n_i$ animals, of which $Y_i$ subsequently respond with positive outcome. An example of real data from such an experiment is shown in the next table, wherein twenty animals were tested, five at each of four dose levels.\n",
    "\n",
    "| Dose, $X_i$ (log g/ml) | Number of animals, $n_i$ | Number of deaths, $Y_i$ |\n",
    "|:----------------------:|:------------------------:|:-----------------------:|\n",
    "| -0.86 | 5 | 0 |\n",
    "| -0.3  | 5 | 1 |\n",
    "| -0.05 | 5 | 3 |\n",
    "| 0.73  | 5 | 5 |\n",
    "\n",
    "Data from *Racine et al. (1986). Bayesian methods in practice: experiences in the pharmaceutical industry (with discussion). Applied Statistics*.\n",
    "\n",
    "For these data, we could consider the following model $$Y_i|\\theta_i\\sim\\textsf{Binomial}(n_i,\\theta_i),$$ where $\\theta_i$ is the probability of death for animals given dose $x_i$. The simplest model of the dose–response relation --that is, the relation of $\\theta_i$ to $X_i$-- is linear: $\\theta_i = \\alpha + \\beta X_i$. Unfortunately, this model has the flaw that at low or high doses (recall that the dose is measured on the log scale), $\\theta_i$ approaches $\\pm \\infty$, whereas $\\theta_i$, being a probability, must be constrained to lie between 0 and 1. The standard solution is to use a transformation of the $\\theta$’s, such as the logistic, in the dose–response relation: $$\\text{logit}(\\theta_i)=\\alpha+\\beta X_i,$$ where $\\text{logit}(\\theta_i)=\\log(\\theta_i/(1-\\theta_i))$. This is called a logistic regression model."
   ]
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   "source": [
    "Thus, the likelihood for this data would be $$p(\\mathbf{Y}|\\alpha,\\beta)=\\prod_{i=1}^k p(Y_i|\\theta_i)$$ $$p(\\mathbf{Y}|\\alpha,\\beta)=\\prod_{i=1}^k \\theta_i^{Y_i}(1-\\theta_i)^{n_i-Y_i}.$$ For the (hyper)prior we consider $p(\\alpha,\\beta)\\propto 1$."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A parameter of common interest in bioassay studies is the LD50--the dose level at which the probability of death is 50%. In our logistic model, a 50% survival rate means $$\\text{LD50: }\\theta = \\text{logit}^{-1}(\\alpha+\\beta x)=0.5;$$\n",
    "thus, $\\alpha + \\beta x = \\text{logit}(0.5) = 0$, and the LD50 is $x = -\\alpha/\\beta$."
   ]
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   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [],
   "source": [
    "XDose    = np.array([-0.86, -0.3, -0.05, 0.73])\n",
    "NAnimals = np.array([5, 5, 5, 5])\n",
    "YDeath   = np.array([0, 1, 3, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as ModelBioAssay:\n",
    "    Alpha = pm.Flat('Alpha')\n",
    "    Beta  = pm.Flat('Beta')\n",
    "    \n",
    "    Theta = pm.Deterministic('Theta', pm.math.invlogit(Alpha + Beta * XDose))\n",
    "    LD50 = pm.Deterministic('LD50', -Alpha/Beta)\n",
    "\n",
    "    Y = pm.Binomial('Y', n=NAnimals, p=Theta, observed=YDeath)"
   ]
  },
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   "execution_count": 5,
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    "pm.model_to_graphviz(ModelBioAssay)"
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   "execution_count": 6,
   "metadata": {},
   "outputs": [
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      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [Alpha, Beta]\n"
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       "      100.00% [8000/8000 00:03&lt;00:00 Sampling 4 chains, 0 divergences]\n",
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      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 4 seconds.\n"
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   "source": [
    "with ModelBioAssay:\n",
    "    TraceBioAssay = pm.sample()"
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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_pair(TraceBioAssay, var_names=['Alpha', 'Beta'], kind='hexbin', hexbin_kwargs={'cmap':'RdGy_r'}, labeller=labeller, marginals=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': '$\\\\alpha$'}>,\n",
       "       <Axes: title={'center': '$\\\\beta$'}>], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1472x552 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(TraceBioAssay, var_names=['Alpha', 'Beta'], kind='hist', labeller=labeller)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.6, 0.5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(TraceBioAssay, var_names=['LD50'], kind='hist')\n",
    "plt.xlim(-0.6,0.5)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
  },
  "kernelspec": {
   "display_name": "Python 3.10.2 64-bit",
   "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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}