{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "18fb5f57",
   "metadata": {},
   "source": [
    "Prentice analysis across SUV percentiles\n",
    "\n",
    "\n",
    "In this notebook, we extend our Prentice surrogate analysis beyond a single biomarker (e.g., SUV95) to a range of lung SUV percentiles.\n",
    "\n",
    "For each percentile, we define the patient-level biomarker as the maximum value across visits, ensuring a consistent summary per patient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c123153",
   "metadata": {},
   "outputs": [],
   "source": [
    "import prentice_analysis as pa\n",
    "\n",
    "data = pa.load_prentice_mat(\n",
    "    \"flags_combined.mat\",\n",
    "    \"suv_percentilesSLOthenUWM.mat\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30fafcc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['patient_ids', 'flags', 'days', 'lung'])\n",
      "Flags shape: (71, 7)\n",
      "Lung shape: (71, 17, 100)\n"
     ]
    }
   ],
   "source": [
    "print(data.keys())\n",
    "\n",
    "print(\"Flags shape:\", data[\"flags\"].shape)\n",
    "print(\"Lung shape:\", data[\"lung\"].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "512af8fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentiles = list(range(5, 101, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1c68a740",
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
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       "      <th></th>\n",
       "      <th>Patient_ID</th>\n",
       "      <th>Treatment_raw</th>\n",
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       "      <th>Lung_SUV_5_max</th>\n",
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      ],
      "text/plain": [
       "  Patient_ID  Treatment_raw  AE_raw  Lung_SUV_5_max  Lung_SUV_10_max  \\\n",
       "0     112_14              1       0        0.523099         0.604090   \n",
       "1    1127_06              1       1        0.389245         0.428076   \n",
       "2    1414_15              1       0        0.377782         0.436540   \n",
       "3    1705_16              1       0        0.281353         0.314005   \n",
       "4    2278_07              1       0        0.510499         0.578509   \n",
       "\n",
       "   Lung_SUV_15_max  Lung_SUV_20_max  Lung_SUV_25_max  Lung_SUV_30_max  \\\n",
       "0         0.676455         0.747582         0.819031         0.887717   \n",
       "1         0.456446         0.480869         0.503969         0.526666   \n",
       "2         0.477014         0.511301         0.543089         0.573795   \n",
       "3         0.340278         0.364427         0.389271         0.415832   \n",
       "4         0.629201         0.671827         0.711613         0.750842   \n",
       "\n",
       "   Lung_SUV_35_max  ...  Lung_SUV_55_max  Lung_SUV_60_max  Lung_SUV_65_max  \\\n",
       "0         0.952473  ...         1.182815         1.241659         1.304624   \n",
       "1         0.550218  ...         0.666177         0.703120         0.745086   \n",
       "2         0.604142  ...         0.737312         0.776308         0.818474   \n",
       "3         0.444415  ...         0.576879         0.614590         0.655718   \n",
       "4         0.789564  ...         0.948465         0.992475         1.038913   \n",
       "\n",
       "   Lung_SUV_70_max  Lung_SUV_75_max  Lung_SUV_80_max  Lung_SUV_85_max  \\\n",
       "0         1.373675         1.450774         1.541891         1.654866   \n",
       "1         0.791398         0.844671         0.924240         1.042221   \n",
       "2         0.864502         0.915714         0.974220         1.042171   \n",
       "3         0.701251         0.752991         0.814760         0.889926   \n",
       "4         1.090024         1.149026         1.220272         1.306984   \n",
       "\n",
       "   Lung_SUV_90_max  Lung_SUV_95_max  Lung_SUV_100_max  \n",
       "0         1.814528         2.105302         55.640804  \n",
       "1         1.232049         1.536383         12.564720  \n",
       "2         1.131384         1.274405          4.290684  \n",
       "3         0.996449         1.187935         19.854958  \n",
       "4         1.419416         1.674636          9.817656  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pa.build_analysis_df_from_mat(\n",
    "    data,\n",
    "    treatment_col=0,   # ← likely treatment\n",
    "    ae_col=5,          # ← likely AE\n",
    "    percentiles=percentiles\n",
    ")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "20a41cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Patient_ID</th>\n",
       "      <th>Treatment_raw</th>\n",
       "      <th>AE_raw</th>\n",
       "      <th>Lung_SUV_5_max</th>\n",
       "      <th>Lung_SUV_10_max</th>\n",
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      ],
      "text/plain": [
       "  Patient_ID  Treatment_raw  AE_raw  Lung_SUV_5_max  Lung_SUV_10_max  \\\n",
       "0     112_14              1       0        0.523099         0.604090   \n",
       "1    1127_06              1       1        0.389245         0.428076   \n",
       "2    1414_15              1       0        0.377782         0.436540   \n",
       "3    1705_16              1       0        0.281353         0.314005   \n",
       "4    2278_07              1       0        0.510499         0.578509   \n",
       "\n",
       "   Lung_SUV_15_max  Lung_SUV_20_max  Lung_SUV_25_max  Lung_SUV_30_max  \\\n",
       "0         0.676455         0.747582         0.819031         0.887717   \n",
       "1         0.456446         0.480869         0.503969         0.526666   \n",
       "2         0.477014         0.511301         0.543089         0.573795   \n",
       "3         0.340278         0.364427         0.389271         0.415832   \n",
       "4         0.629201         0.671827         0.711613         0.750842   \n",
       "\n",
       "   Lung_SUV_35_max  ...  Lung_SUV_65_max  Lung_SUV_70_max  Lung_SUV_75_max  \\\n",
       "0         0.952473  ...         1.304624         1.373675         1.450774   \n",
       "1         0.550218  ...         0.745086         0.791398         0.844671   \n",
       "2         0.604142  ...         0.818474         0.864502         0.915714   \n",
       "3         0.444415  ...         0.655718         0.701251         0.752991   \n",
       "4         0.789564  ...         1.038913         1.090024         1.149026   \n",
       "\n",
       "   Lung_SUV_80_max  Lung_SUV_85_max  Lung_SUV_90_max  Lung_SUV_95_max  \\\n",
       "0         1.541891         1.654866         1.814528         2.105302   \n",
       "1         0.924240         1.042221         1.232049         1.536383   \n",
       "2         0.974220         1.042171         1.131384         1.274405   \n",
       "3         0.814760         0.889926         0.996449         1.187935   \n",
       "4         1.220272         1.306984         1.419416         1.674636   \n",
       "\n",
       "   Lung_SUV_100_max  Treatment  AE  \n",
       "0         55.640804          0   0  \n",
       "1         12.564720          0   1  \n",
       "2          4.290684          0   0  \n",
       "3         19.854958          0   0  \n",
       "4          9.817656          0   0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatment_map = {\n",
    "    1: 0,\n",
    "    2: 0,\n",
    "    3: 0,\n",
    "    5: 1,\n",
    "    9: 1\n",
    "}\n",
    "\n",
    "df[\"Treatment\"] = df[\"Treatment_raw\"].map(treatment_map)\n",
    "df[\"AE\"] = df[\"AE_raw\"]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "22332556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Lung_SUV_5_max', 'Lung_SUV_10_max', 'Lung_SUV_15_max', 'Lung_SUV_20_max', 'Lung_SUV_25_max']\n"
     ]
    }
   ],
   "source": [
    "percentiles = list(range(5, 101, 5))\n",
    "\n",
    "biomarker_cols = [f\"Lung_SUV_{p}_max\" for p in percentiles]\n",
    "\n",
    "print(biomarker_cols[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4729fe28",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\zahra\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>biomarker</th>\n",
       "      <th>C1_p</th>\n",
       "      <th>C2_p</th>\n",
       "      <th>C3_p</th>\n",
       "      <th>C4_Z_p</th>\n",
       "      <th>C4_S_p</th>\n",
       "      <th>PE</th>\n",
       "      <th>PE_risk</th>\n",
       "      <th>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lung_SUV_5_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.180648</td>\n",
       "      <td>0.391947</td>\n",
       "      <td>0.999011</td>\n",
       "      <td>0.447461</td>\n",
       "      <td>-0.873942</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lung_SUV_10_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.220694</td>\n",
       "      <td>0.259521</td>\n",
       "      <td>0.999419</td>\n",
       "      <td>0.299437</td>\n",
       "      <td>-1.721333</td>\n",
       "      <td>-1.037612</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lung_SUV_15_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.253225</td>\n",
       "      <td>0.246258</td>\n",
       "      <td>0.999447</td>\n",
       "      <td>0.283013</td>\n",
       "      <td>-1.852068</td>\n",
       "      <td>-1.118455</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lung_SUV_20_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.277178</td>\n",
       "      <td>0.242275</td>\n",
       "      <td>0.998825</td>\n",
       "      <td>0.277394</td>\n",
       "      <td>-1.904771</td>\n",
       "      <td>-1.149946</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lung_SUV_25_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.296118</td>\n",
       "      <td>0.242687</td>\n",
       "      <td>0.999133</td>\n",
       "      <td>0.276884</td>\n",
       "      <td>-1.919268</td>\n",
       "      <td>-1.156261</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lung_SUV_30_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.314547</td>\n",
       "      <td>0.244748</td>\n",
       "      <td>0.999822</td>\n",
       "      <td>0.278213</td>\n",
       "      <td>-1.919068</td>\n",
       "      <td>-1.155422</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lung_SUV_35_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.331386</td>\n",
       "      <td>0.249599</td>\n",
       "      <td>0.996800</td>\n",
       "      <td>0.282658</td>\n",
       "      <td>-1.893223</td>\n",
       "      <td>-1.143974</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lung_SUV_40_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.347803</td>\n",
       "      <td>0.256964</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.289833</td>\n",
       "      <td>-1.844501</td>\n",
       "      <td>-1.125671</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lung_SUV_45_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.362406</td>\n",
       "      <td>0.266071</td>\n",
       "      <td>0.999768</td>\n",
       "      <td>0.298966</td>\n",
       "      <td>-1.782004</td>\n",
       "      <td>-1.104541</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lung_SUV_50_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.375690</td>\n",
       "      <td>0.275802</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.308821</td>\n",
       "      <td>-1.717557</td>\n",
       "      <td>-1.084817</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lung_SUV_55_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.389215</td>\n",
       "      <td>0.285329</td>\n",
       "      <td>0.999860</td>\n",
       "      <td>0.318440</td>\n",
       "      <td>-1.658327</td>\n",
       "      <td>-1.070200</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lung_SUV_60_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.405060</td>\n",
       "      <td>0.297655</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.330922</td>\n",
       "      <td>-1.582551</td>\n",
       "      <td>-1.043581</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lung_SUV_65_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.423703</td>\n",
       "      <td>0.315889</td>\n",
       "      <td>0.999435</td>\n",
       "      <td>0.349463</td>\n",
       "      <td>-1.470922</td>\n",
       "      <td>-0.992155</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Lung_SUV_70_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.444673</td>\n",
       "      <td>0.344577</td>\n",
       "      <td>0.999936</td>\n",
       "      <td>0.378732</td>\n",
       "      <td>-1.305816</td>\n",
       "      <td>-0.897806</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lung_SUV_75_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.472071</td>\n",
       "      <td>0.379143</td>\n",
       "      <td>0.999890</td>\n",
       "      <td>0.413464</td>\n",
       "      <td>-1.130677</td>\n",
       "      <td>-0.793969</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lung_SUV_80_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.512830</td>\n",
       "      <td>0.432833</td>\n",
       "      <td>0.999782</td>\n",
       "      <td>0.466642</td>\n",
       "      <td>-0.895964</td>\n",
       "      <td>-0.639848</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Lung_SUV_85_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.569874</td>\n",
       "      <td>0.521932</td>\n",
       "      <td>0.853121</td>\n",
       "      <td>0.553942</td>\n",
       "      <td>-0.585186</td>\n",
       "      <td>-0.412484</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Lung_SUV_90_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.635140</td>\n",
       "      <td>0.692811</td>\n",
       "      <td>0.999350</td>\n",
       "      <td>0.721507</td>\n",
       "      <td>-0.205122</td>\n",
       "      <td>-0.135550</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Lung_SUV_95_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.759202</td>\n",
       "      <td>0.950283</td>\n",
       "      <td>0.999454</td>\n",
       "      <td>0.932928</td>\n",
       "      <td>-0.010139</td>\n",
       "      <td>-0.007981</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Lung_SUV_100_max</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>0.843803</td>\n",
       "      <td>0.690510</td>\n",
       "      <td>0.999383</td>\n",
       "      <td>0.703974</td>\n",
       "      <td>-0.254284</td>\n",
       "      <td>-0.204175</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           biomarker     C1_p      C2_p      C3_p    C4_Z_p    C4_S_p  \\\n",
       "0     Lung_SUV_5_max  0.99946  0.180648  0.391947  0.999011  0.447461   \n",
       "1    Lung_SUV_10_max  0.99946  0.220694  0.259521  0.999419  0.299437   \n",
       "2    Lung_SUV_15_max  0.99946  0.253225  0.246258  0.999447  0.283013   \n",
       "3    Lung_SUV_20_max  0.99946  0.277178  0.242275  0.998825  0.277394   \n",
       "4    Lung_SUV_25_max  0.99946  0.296118  0.242687  0.999133  0.276884   \n",
       "5    Lung_SUV_30_max  0.99946  0.314547  0.244748  0.999822  0.278213   \n",
       "6    Lung_SUV_35_max  0.99946  0.331386  0.249599  0.996800  0.282658   \n",
       "7    Lung_SUV_40_max  0.99946  0.347803  0.256964  1.000000  0.289833   \n",
       "8    Lung_SUV_45_max  0.99946  0.362406  0.266071  0.999768  0.298966   \n",
       "9    Lung_SUV_50_max  0.99946  0.375690  0.275802  0.999995  0.308821   \n",
       "10   Lung_SUV_55_max  0.99946  0.389215  0.285329  0.999860  0.318440   \n",
       "11   Lung_SUV_60_max  0.99946  0.405060  0.297655  0.999998  0.330922   \n",
       "12   Lung_SUV_65_max  0.99946  0.423703  0.315889  0.999435  0.349463   \n",
       "13   Lung_SUV_70_max  0.99946  0.444673  0.344577  0.999936  0.378732   \n",
       "14   Lung_SUV_75_max  0.99946  0.472071  0.379143  0.999890  0.413464   \n",
       "15   Lung_SUV_80_max  0.99946  0.512830  0.432833  0.999782  0.466642   \n",
       "16   Lung_SUV_85_max  0.99946  0.569874  0.521932  0.853121  0.553942   \n",
       "17   Lung_SUV_90_max  0.99946  0.635140  0.692811  0.999350  0.721507   \n",
       "18   Lung_SUV_95_max  0.99946  0.759202  0.950283  0.999454  0.932928   \n",
       "19  Lung_SUV_100_max  0.99946  0.843803  0.690510  0.999383  0.703974   \n",
       "\n",
       "          PE   PE_risk     C1     C2     C3     C4  \n",
       "0  -0.873942 -0.473302  False  False  False  False  \n",
       "1  -1.721333 -1.037612  False  False  False  False  \n",
       "2  -1.852068 -1.118455  False  False  False  False  \n",
       "3  -1.904771 -1.149946  False  False  False  False  \n",
       "4  -1.919268 -1.156261  False  False  False  False  \n",
       "5  -1.919068 -1.155422  False  False  False  False  \n",
       "6  -1.893223 -1.143974  False  False  False  False  \n",
       "7  -1.844501 -1.125671  False  False  False  False  \n",
       "8  -1.782004 -1.104541  False  False  False  False  \n",
       "9  -1.717557 -1.084817  False  False  False  False  \n",
       "10 -1.658327 -1.070200  False  False  False  False  \n",
       "11 -1.582551 -1.043581  False  False  False  False  \n",
       "12 -1.470922 -0.992155  False  False  False  False  \n",
       "13 -1.305816 -0.897806  False  False  False  False  \n",
       "14 -1.130677 -0.793969  False  False  False  False  \n",
       "15 -0.895964 -0.639848  False  False  False  False  \n",
       "16 -0.585186 -0.412484  False  False  False  False  \n",
       "17 -0.205122 -0.135550  False  False  False  False  \n",
       "18 -0.010139 -0.007981  False  False  False  False  \n",
       "19 -0.254284 -0.204175  False  False  False  False  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pa.run_prentice_multiple(\n",
    "    df,\n",
    "    biomarker_cols,\n",
    "    treatment_col=\"Treatment\",\n",
    "    ae_col=\"AE\"\n",
    ")\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43e472e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "COL_PATIENT = \"Patient_ID\"\n",
    "COL_TREAT   = \"Treatment\"\n",
    "COL_AE      = \"Lung_AE_Flag\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f11f89f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4d572dd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "results[\"PE\"] = pd.to_numeric(results[\"PE\"], errors=\"coerce\")\n",
    "results[\"PE_risk\"] = pd.to_numeric(results[\"PE_risk\"], errors=\"coerce\")\n",
    "results[\"C1_p\"] = pd.to_numeric(results[\"C1_p\"], errors=\"coerce\")\n",
    "results[\"C2_p\"] = pd.to_numeric(results[\"C2_p\"], errors=\"coerce\")\n",
    "results[\"C3_p\"] = pd.to_numeric(results[\"C3_p\"], errors=\"coerce\")\n",
    "results[\"C4_S_p\"] = pd.to_numeric(results[\"C4_S_p\"], errors=\"coerce\")\n",
    "results[\"C4_Z_p\"] = pd.to_numeric(results[\"C4_Z_p\"], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fbac45ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 840x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4), dpi=140)\n",
    "plt.plot(percentiles, results[\"PE\"], marker=\"o\", linewidth=1.8)\n",
    "plt.xlabel(\"SUV percentile\")\n",
    "plt.ylabel(\"PE\")\n",
    "plt.title(\"Freedman PE across SUV percentiles\")\n",
    "plt.grid(alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5c69fecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 840x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4), dpi=140)\n",
    "plt.plot(percentiles, results[\"PE_risk\"], marker=\"o\", linewidth=1.8)\n",
    "plt.xlabel(\"SUV percentile\")\n",
    "plt.ylabel(\"PE_risk\")\n",
    "plt.title(\"Wang PE_risk across SUV percentiles\")\n",
    "plt.grid(alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e71de258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 980x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "\n",
    "c1 = (results[\"C1_p\"] < alpha).astype(int)\n",
    "c2 = (results[\"C2_p\"] < alpha).astype(int)\n",
    "c3 = (results[\"C3_p\"] < alpha).astype(int)\n",
    "c4 = ((results[\"C4_S_p\"] < alpha) & (results[\"C4_Z_p\"] > alpha)).astype(int)\n",
    "\n",
    "plt.figure(figsize=(7, 4), dpi=140)\n",
    "plt.plot(percentiles, c1, marker=\"o\", label=\"C1\")\n",
    "plt.plot(percentiles, c2, marker=\"s\", label=\"C2\")\n",
    "plt.plot(percentiles, c3, marker=\"^\", label=\"C3\")\n",
    "plt.plot(percentiles, c4, marker=\"d\", label=\"C4\")\n",
    "plt.xlabel(\"SUV percentile\")\n",
    "plt.ylabel(\"Criterion met\")\n",
    "plt.yticks([0, 1], [\"No\", \"Yes\"])\n",
    "plt.title(\"Prentice criteria across SUV percentiles\")\n",
    "plt.grid(alpha=0.3)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5ca23973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 910x588 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6.5, 4.2), dpi=140)\n",
    "plt.plot(percentiles, results[\"PE\"], marker=\"o\", linewidth=1.8, label=\"PE\")\n",
    "plt.plot(percentiles, results[\"PE_risk\"], marker=\"s\", linewidth=1.8, label=\"PE_risk\")\n",
    "plt.xlabel(\"SUV percentile\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.title(\"PE and PE_risk across SUV percentiles\")\n",
    "plt.grid(alpha=0.3)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e75ff3d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best PE percentile:\n",
      "biomarker    Lung_SUV_95_max\n",
      "PE                 -0.010139\n",
      "PE_risk            -0.007981\n",
      "C1_p                 0.99946\n",
      "C2_p                0.759202\n",
      "C3_p                0.950283\n",
      "C4_S_p              0.932928\n",
      "C4_Z_p              0.999454\n",
      "Name: 18, dtype: object\n",
      "\n",
      "Best PE_risk percentile:\n",
      "biomarker    Lung_SUV_95_max\n",
      "PE                 -0.010139\n",
      "PE_risk            -0.007981\n",
      "C1_p                 0.99946\n",
      "C2_p                0.759202\n",
      "C3_p                0.950283\n",
      "C4_S_p              0.932928\n",
      "C4_Z_p              0.999454\n",
      "Name: 18, dtype: object\n"
     ]
    }
   ],
   "source": [
    "best_pe_idx = results[\"PE\"].idxmax()\n",
    "best_perisk_idx = results[\"PE_risk\"].idxmax()\n",
    "\n",
    "print(\"Best PE percentile:\")\n",
    "print(results.loc[best_pe_idx, [\"biomarker\", \"PE\", \"PE_risk\", \"C1_p\", \"C2_p\", \"C3_p\", \"C4_S_p\", \"C4_Z_p\"]])\n",
    "\n",
    "print(\"\\nBest PE_risk percentile:\")\n",
    "print(results.loc[best_perisk_idx, [\"biomarker\", \"PE\", \"PE_risk\", \"C1_p\", \"C2_p\", \"C3_p\", \"C4_S_p\", \"C4_Z_p\"]])"
   ]
  }
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