Adding results.

prentice_criteria.ipynb

@@ -174,7 +174,7 @@
          "type": "string"
         }
        ],
-       "ref": "fa6f2da9-e7ce-4d2b-a902-6ed191c7e0b1",
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@@ -389,7 +389,7 @@
          "type": "integer"
         }
        ],
-       "ref": "65f5bd4f-e917-4132-af97-1aa54ad404c6",
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@@ -560,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "0adcb6fe",
    "metadata": {},
    "outputs": [],
@@ -601,7 +601,7 @@
          "type": "integer"
         }
        ],
-       "ref": "6a990d52-0086-4c59-8aa5-d7acc2d83825",
+       "ref": "ca8fd6c7-b9dd-4788-ac6f-2e13a8e6fefd",
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@@ -693,7 +693,7 @@
          "type": "float"
         }
        ],
-       "ref": "431bd473-d76e-451e-ab03-9ca002c3a0c4",
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        "rows": [
         [
          "0",
@@ -790,6 +790,9 @@
     "\n",
     "plt.xlabel('T')\n",
     "plt.ylabel('Z')\n",
+    "\n",
+    "plt.savefig(\"results/contingency_table.png\", dpi=300, bbox_inches='tight')\n",
+    "\n",
     "plt.show()"
    ]
   },
@@ -847,7 +850,7 @@
          "type": "float"
         }
        ],
-       "ref": "b07a4aad-7af4-4c42-9300-d0d0733cf936",
+       "ref": "fba2a65e-834d-485a-a68c-7f0982111e5b",
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@@ -952,7 +955,7 @@
          "type": "float"
         }
        ],
-       "ref": "80760edf-29c5-49fb-90a7-e16f1026f646",
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        "rows": [
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@@ -1044,7 +1047,7 @@
          "type": "float"
         }
        ],
-       "ref": "eb14d2c3-8c28-47e2-b4b8-979e7b202dde",
+       "ref": "ba2f7e51-78dd-4a7f-b472-8d9205308ef4",
        "rows": [
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@@ -1322,7 +1325,7 @@
          "type": "boolean"
         }
        ],
-       "ref": "e26dba4c-dc0c-4b6f-855a-e61accb0f5a1",
+       "ref": "52915e30-0084-4139-9619-614639d91171",
        "rows": [
         [
          "0",
@@ -1474,7 +1477,7 @@
     }
    ],
    "source": [
-    "df_prentice = utils.prentice_criteria(\n",
+    "df_prentice1 = utils.prentice_criteria(\n",
     "    df_work,\n",
     "    endpoint=\"T\",\n",
     "    surrogate=\"S\",\n",
@@ -1485,7 +1488,219 @@
     "    criterion2_test=\"ks\",\n",
     ")\n",
     "\n",
-    "df_prentice"
+    "df_prentice1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "463b5b1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Criterion",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Method",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Model",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Estimate",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "P-value",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "Pass",
+         "rawType": "boolean",
+         "type": "boolean"
+        }
+       ],
+       "ref": "1998c6dd-7ade-4525-b555-7017507076e6",
+       "rows": [
+        [
+         "0",
+         "1. Treatment -> True",
+         "Fisher's exact test",
+         "Z x T (2x2 table)",
+         "0.6720937713621131",
+         "0.4873416561256851",
+         "False"
+        ],
+        [
+         "1",
+         "2. Treatment -> Surrogate",
+         "Kolmogorov–Smirnov test",
+         "S by Z groups",
+         "0.28851540616246496",
+         "0.5879664894834571",
+         "False"
+        ],
+        [
+         "2",
+         "3. Surrogate -> True",
+         "Logistic",
+         "T ~ S",
+         "10.73815933915853",
+         "0.004114055500597262",
+         "True"
+        ],
+        [
+         "3",
+         "4. Full Mediation",
+         "Logistic",
+         "T ~ S + Z",
+         "1.0343270483046114",
+         "0.6441919482170702",
+         "True"
+        ],
+        [
+         "4",
+         "Proportion Explained (PE)",
+         "Logistic coefficients",
+         "Compare Z in (T ~ Z) vs (T ~ S + Z).",
+         "-0.5389624072967832",
+         null,
+         "False"
+        ]
+       ],
+       "shape": {
+        "columns": 6,
+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Criterion</th>\n",
+       "      <th>Method</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Estimate</th>\n",
+       "      <th>P-value</th>\n",
+       "      <th>Pass</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1. Treatment -&gt; True</td>\n",
+       "      <td>Fisher's exact test</td>\n",
+       "      <td>Z x T (2x2 table)</td>\n",
+       "      <td>0.672094</td>\n",
+       "      <td>0.487342</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2. Treatment -&gt; Surrogate</td>\n",
+       "      <td>Kolmogorov–Smirnov test</td>\n",
+       "      <td>S by Z groups</td>\n",
+       "      <td>0.288515</td>\n",
+       "      <td>0.587966</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3. Surrogate -&gt; True</td>\n",
+       "      <td>Logistic</td>\n",
+       "      <td>T ~ S</td>\n",
+       "      <td>10.738159</td>\n",
+       "      <td>0.004114</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4. Full Mediation</td>\n",
+       "      <td>Logistic</td>\n",
+       "      <td>T ~ S + Z</td>\n",
+       "      <td>1.034327</td>\n",
+       "      <td>0.644192</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Proportion Explained (PE)</td>\n",
+       "      <td>Logistic coefficients</td>\n",
+       "      <td>Compare Z in (T ~ Z) vs (T ~ S + Z).</td>\n",
+       "      <td>-0.538962</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Criterion                   Method  \\\n",
+       "0       1. Treatment -> True      Fisher's exact test   \n",
+       "1  2. Treatment -> Surrogate  Kolmogorov–Smirnov test   \n",
+       "2       3. Surrogate -> True                 Logistic   \n",
+       "3          4. Full Mediation                 Logistic   \n",
+       "4  Proportion Explained (PE)    Logistic coefficients   \n",
+       "\n",
+       "                                  Model   Estimate   P-value   Pass  \n",
+       "0                     Z x T (2x2 table)   0.672094  0.487342  False  \n",
+       "1                         S by Z groups   0.288515  0.587966  False  \n",
+       "2                                 T ~ S  10.738159  0.004114   True  \n",
+       "3                             T ~ S + Z   1.034327  0.644192   True  \n",
+       "4  Compare Z in (T ~ Z) vs (T ~ S + Z).  -0.538962      <NA>  False  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_prentice2 = utils.prentice_criteria(\n",
+    "    df_work,\n",
+    "    endpoint=\"T\",\n",
+    "    surrogate=\"S\",\n",
+    "    treatment=\"Z\",\n",
+    "    alpha=0.05,\n",
+    "    return_models=False,\n",
+    "    criterion1_test=\"fisher\",\n",
+    "    criterion2_test=\"ks\",\n",
+    ")\n",
+    "\n",
+    "df_prentice2"
    ]
   },
   {
@@ -1494,7 +1709,12 @@
    "id": "469afd91",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# storing results in an Excel file with two sheets\n",
+    "with pd.ExcelWriter(\"results/prentice_results.xlsx\", engine=\"openpyxl\") as writer:\n",
+    "    df_prentice1.to_excel(writer, sheet_name=\"chi2_ks\", index=False)\n",
+    "    df_prentice2.to_excel(writer, sheet_name=\"fisher_ks\", index=False)"
+   ]
   }
  ],
  "metadata": {