{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Meta-Analysis in statsmodels\n",
    "\n",
    "Statsmodels include basic methods for meta-analysis. This notebook illustrates the current usage.\n",
    "\n",
    "Status: The results have been verified against R meta and metafor packages. However, the API is still experimental and will still change. Some options for additional methods that are available in R meta and metafor are missing.\n",
    "\n",
    "The support for meta-analysis has 3 parts:\n",
    "\n",
    "- effect size functions: this currently includes\n",
    "  ``effectsize_smd`` computes effect size and their standard errors for standardized mean difference,  \n",
    "  ``effectsize_2proportions`` computes effect sizes for comparing two independent proportions using risk difference, (log) risk ratio, (log) odds-ratio or arcsine square root transformation\n",
    "- The `combine_effects` computes fixed and random effects estimate for the overall mean or effect. The returned results instance includes a forest plot function.\n",
    "- helper functions to estimate the random effect variance, tau-squared\n",
    "\n",
    "The estimate of the overall effect size in `combine_effects` can also be performed using WLS or GLM with var_weights.\n",
    "\n",
    "Finally, the meta-analysis functions currently do not include the Mantel-Hanszel method. However, the fixed effects results can be computed directly using `StratifiedTable` as illustrated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:53.456374Z",
     "iopub.status.busy": "2026-07-29T11:35:53.456177Z",
     "iopub.status.idle": "2026-07-29T11:35:54.365983Z",
     "shell.execute_reply": "2026-07-29T11:35:54.364980Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:54.368566Z",
     "iopub.status.busy": "2026-07-29T11:35:54.367942Z",
     "iopub.status.idle": "2026-07-29T11:35:56.025224Z",
     "shell.execute_reply": "2026-07-29T11:35:56.020445Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from statsmodels.genmod.generalized_linear_model import GLM\n",
    "from statsmodels.stats.meta_analysis import (\n",
    "    combine_effects,\n",
    "    effectsize_2proportions,\n",
    "    effectsize_smd,\n",
    ")\n",
    "\n",
    "# increase line length for pandas\n",
    "pd.set_option(\"display.width\", 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.027802Z",
     "iopub.status.busy": "2026-07-29T11:35:56.027311Z",
     "iopub.status.idle": "2026-07-29T11:35:56.039170Z",
     "shell.execute_reply": "2026-07-29T11:35:56.036984Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Carroll', 'Grant', 'Peck', 'Donat', 'Stewart', 'Young']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "    [\"Carroll\", 94, 22, 60, 92, 20, 60],\n",
    "    [\"Grant\", 98, 21, 65, 92, 22, 65],\n",
    "    [\"Peck\", 98, 28, 40, 88, 26, 40],\n",
    "    [\"Donat\", 94, 19, 200, 82, 17, 200],\n",
    "    [\"Stewart\", 98, 21, 50, 88, 22, 45],\n",
    "    [\"Young\", 96, 21, 85, 92, 22, 85],\n",
    "]\n",
    "colnames = [\"study\", \"mean_t\", \"sd_t\", \"n_t\", \"mean_c\", \"sd_c\", \"n_c\"]\n",
    "rownames = [i[0] for i in data]\n",
    "dframe1 = pd.DataFrame(data, columns=colnames)\n",
    "rownames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.041296Z",
     "iopub.status.busy": "2026-07-29T11:35:56.041078Z",
     "iopub.status.idle": "2026-07-29T11:35:56.050306Z",
     "shell.execute_reply": "2026-07-29T11:35:56.049023Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Carroll', 'Grant', 'Peck', 'Donat', 'Stewart', 'Young']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean2, sd2, nobs2, mean1, sd1, nobs1 = np.asarray(\n",
    "    dframe1[[\"mean_t\", \"sd_t\", \"n_t\", \"mean_c\", \"sd_c\", \"n_c\"]]\n",
    ").T\n",
    "rownames = dframe1[\"study\"]\n",
    "rownames.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.053070Z",
     "iopub.status.busy": "2026-07-29T11:35:56.052206Z",
     "iopub.status.idle": "2026-07-29T11:35:56.060336Z",
     "shell.execute_reply": "2026-07-29T11:35:56.058154Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([120, 130,  80, 400,  95, 170])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(nobs1 + nobs2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### estimate effect size standardized mean difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.065433Z",
     "iopub.status.busy": "2026-07-29T11:35:56.065226Z",
     "iopub.status.idle": "2026-07-29T11:35:56.069479Z",
     "shell.execute_reply": "2026-07-29T11:35:56.068662Z"
    }
   },
   "outputs": [],
   "source": [
    "eff, var_eff = effectsize_smd(mean2, sd2, nobs2, mean1, sd1, nobs1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using one-step chi2, DerSimonian-Laird estimate for random effects variance tau\n",
    "\n",
    "Method option for random effect `method_re=\"chi2\"` or `method_re=\"dl\"`, both names are accepted.\n",
    "This is commonly referred to as the DerSimonian-Laird method, it is based on a moment estimator based on pearson chi2 from the fixed effects estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.073422Z",
     "iopub.status.busy": "2026-07-29T11:35:56.073219Z",
     "iopub.status.idle": "2026-07-29T11:35:56.094356Z",
     "shell.execute_reply": "2026-07-29T11:35:56.091306Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        eff    sd_eff    ci_low    ci_upp      w_fe      w_re\n",
      "Carroll            0.094524  0.182680 -0.267199  0.456248  0.123885  0.157529\n",
      "Grant              0.277356  0.176279 -0.071416  0.626129  0.133045  0.162828\n",
      "Peck               0.366546  0.225573 -0.082446  0.815538  0.081250  0.126223\n",
      "Donat              0.664385  0.102748  0.462389  0.866381  0.391606  0.232734\n",
      "Stewart            0.461808  0.208310  0.048203  0.875413  0.095275  0.137949\n",
      "Young              0.185165  0.153729 -0.118312  0.488641  0.174939  0.182736\n",
      "fixed effect       0.414961  0.064298  0.249677  0.580245  1.000000       NaN\n",
      "random effect      0.358486  0.105462  0.087388  0.629583       NaN  1.000000\n",
      "fixed effect wls   0.414961  0.099237  0.159864  0.670058  1.000000       NaN\n",
      "random effect wls  0.358486  0.090328  0.126290  0.590682       NaN  1.000000\n"
     ]
    }
   ],
   "source": [
    "res3 = combine_effects(eff, var_eff, method_re=\"chi2\", use_t=True, row_names=rownames)\n",
    "# TODO: we still need better information about conf_int of individual samples\n",
    "# We don't have enough information in the model for individual confidence intervals\n",
    "# if those are not based on normal distribution.\n",
    "res3.conf_int_samples(nobs=np.array(nobs1 + nobs2))\n",
    "print(res3.summary_frame())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.099827Z",
     "iopub.status.busy": "2026-07-29T11:35:56.099556Z",
     "iopub.status.idle": "2026-07-29T11:35:56.108931Z",
     "shell.execute_reply": "2026-07-29T11:35:56.106371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0.05,\n",
       "  True): (array([-0.26719942, -0.07141628, -0.08244568,  0.46238908,  0.04820269,\n",
       "         -0.1183121 ]), array([0.45624817, 0.62612908, 0.81553838, 0.86638112, 0.87541326,\n",
       "         0.48864139]))}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res3.cache_ci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.114641Z",
     "iopub.status.busy": "2026-07-29T11:35:56.114422Z",
     "iopub.status.idle": "2026-07-29T11:35:56.125660Z",
     "shell.execute_reply": "2026-07-29T11:35:56.124768Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'chi2'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res3.method_re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.132544Z",
     "iopub.status.busy": "2026-07-29T11:35:56.132340Z",
     "iopub.status.idle": "2026-07-29T11:35:56.382366Z",
     "shell.execute_reply": "2026-07-29T11:35:56.381361Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res3.plot_forest()\n",
    "fig.set_figheight(6)\n",
    "fig.set_figwidth(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.384851Z",
     "iopub.status.busy": "2026-07-29T11:35:56.384608Z",
     "iopub.status.idle": "2026-07-29T11:35:56.395776Z",
     "shell.execute_reply": "2026-07-29T11:35:56.394466Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        eff    sd_eff    ci_low    ci_upp      w_fe      w_re\n",
      "Carroll            0.094524  0.182680 -0.263521  0.452570  0.123885  0.157529\n",
      "Grant              0.277356  0.176279 -0.068144  0.622857  0.133045  0.162828\n",
      "Peck               0.366546  0.225573 -0.075569  0.808662  0.081250  0.126223\n",
      "Donat              0.664385  0.102748  0.463002  0.865768  0.391606  0.232734\n",
      "Stewart            0.461808  0.208310  0.053527  0.870089  0.095275  0.137949\n",
      "Young              0.185165  0.153729 -0.116139  0.486468  0.174939  0.182736\n",
      "fixed effect       0.414961  0.064298  0.288939  0.540984  1.000000       NaN\n",
      "random effect      0.358486  0.105462  0.151785  0.565187       NaN  1.000000\n",
      "fixed effect wls   0.414961  0.099237  0.220460  0.609462  1.000000       NaN\n",
      "random effect wls  0.358486  0.090328  0.181446  0.535526       NaN  1.000000\n"
     ]
    }
   ],
   "source": [
    "res3 = combine_effects(eff, var_eff, method_re=\"chi2\", use_t=False, row_names=rownames)\n",
    "# TODO: we still need better information about conf_int of individual samples\n",
    "# We don't have enough information in the model for individual confidence intervals\n",
    "# if those are not based on normal distribution.\n",
    "res3.conf_int_samples(nobs=np.array(nobs1 + nobs2))\n",
    "print(res3.summary_frame())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using iterated, Paule-Mandel estimate for random effects variance tau\n",
    "\n",
    "The method commonly referred to as Paule-Mandel estimate is a method of moment estimate for the random effects variance that iterates between mean and variance estimate until convergence.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.398601Z",
     "iopub.status.busy": "2026-07-29T11:35:56.397764Z",
     "iopub.status.idle": "2026-07-29T11:35:56.621527Z",
     "shell.execute_reply": "2026-07-29T11:35:56.620140Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method RE: iterated\n",
      "                        eff    sd_eff    ci_low    ci_upp      w_fe      w_re\n",
      "Carroll            0.094524  0.182680 -0.263521  0.452570  0.123885  0.152619\n",
      "Grant              0.277356  0.176279 -0.068144  0.622857  0.133045  0.159157\n",
      "Peck               0.366546  0.225573 -0.075569  0.808662  0.081250  0.116228\n",
      "Donat              0.664385  0.102748  0.463002  0.865768  0.391606  0.257767\n",
      "Stewart            0.461808  0.208310  0.053527  0.870089  0.095275  0.129428\n",
      "Young              0.185165  0.153729 -0.116139  0.486468  0.174939  0.184799\n",
      "fixed effect       0.414961  0.064298  0.288939  0.540984  1.000000       NaN\n",
      "random effect      0.366419  0.092390  0.185338  0.547500       NaN  1.000000\n",
      "fixed effect wls   0.414961  0.099237  0.220460  0.609462  1.000000       NaN\n",
      "random effect wls  0.366419  0.092390  0.185338  0.547500       NaN  1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res4 = combine_effects(\n",
    "    eff, var_eff, method_re=\"iterated\", use_t=False, row_names=rownames\n",
    ")\n",
    "res4_df = res4.summary_frame()\n",
    "print(\"method RE:\", res4.method_re)\n",
    "print(res4.summary_frame())\n",
    "fig = res4.plot_forest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Kacker interlaboratory mean\n",
    "\n",
    "In this example the effect size is the mean of measurements in a lab. We combine the estimates from several labs to estimate and overall average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.623660Z",
     "iopub.status.busy": "2026-07-29T11:35:56.623431Z",
     "iopub.status.idle": "2026-07-29T11:35:56.629301Z",
     "shell.execute_reply": "2026-07-29T11:35:56.628118Z"
    }
   },
   "outputs": [],
   "source": [
    "eff = np.array([61.00, 61.40, 62.21, 62.30, 62.34, 62.60, 62.70, 62.84, 65.90])\n",
    "var_eff = np.array(\n",
    "    [0.2025, 1.2100, 0.0900, 0.2025, 0.3844, 0.5625, 0.0676, 0.0225, 1.8225]\n",
    ")\n",
    "rownames = [\"PTB\", \"NMi\", \"NIMC\", \"KRISS\", \"LGC\", \"NRC\", \"IRMM\", \"NIST\", \"LNE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.631444Z",
     "iopub.status.busy": "2026-07-29T11:35:56.631226Z",
     "iopub.status.idle": "2026-07-29T11:35:56.847642Z",
     "shell.execute_reply": "2026-07-29T11:35:56.846082Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method RE: dl\n",
      "                         eff    sd_eff     ci_low     ci_upp      w_fe      w_re\n",
      "PTB                61.000000  0.450000  60.118016  61.881984  0.057436  0.123113\n",
      "NMi                61.400000  1.100000  59.244040  63.555960  0.009612  0.040314\n",
      "NIMC               62.210000  0.300000  61.622011  62.797989  0.129230  0.159749\n",
      "KRISS              62.300000  0.450000  61.418016  63.181984  0.057436  0.123113\n",
      "LGC                62.340000  0.620000  61.124822  63.555178  0.030257  0.089810\n",
      "NRC                62.600000  0.750000  61.130027  64.069973  0.020677  0.071005\n",
      "IRMM               62.700000  0.260000  62.190409  63.209591  0.172052  0.169810\n",
      "NIST               62.840000  0.150000  62.546005  63.133995  0.516920  0.194471\n",
      "LNE                65.900000  1.350000  63.254049  68.545951  0.006382  0.028615\n",
      "fixed effect       62.583397  0.107846  62.334704  62.832090  1.000000       NaN\n",
      "random effect      62.390139  0.245750  61.823439  62.956838       NaN  1.000000\n",
      "fixed effect wls   62.583397  0.189889  62.145512  63.021282  1.000000       NaN\n",
      "random effect wls  62.390139  0.294776  61.710384  63.069893       NaN  1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/hostedtoolcache/Python/3.14.6/x64/lib/python3.14/site-packages/statsmodels/stats/meta_analysis.py:231: InvalidTestWarning: `use_t=True` requires `nobs` for each sample or `ci_func`. Using normal distribution for confidence interval of individual samples.\n",
      "  ci_low, ci_upp = self.conf_int_samples(alpha=alpha, use_t=use_t)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res2_DL = combine_effects(eff, var_eff, method_re=\"dl\", use_t=True, row_names=rownames)\n",
    "print(\"method RE:\", res2_DL.method_re)\n",
    "print(res2_DL.summary_frame())\n",
    "fig = res2_DL.plot_forest()\n",
    "fig.set_figheight(6)\n",
    "fig.set_figwidth(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:56.849984Z",
     "iopub.status.busy": "2026-07-29T11:35:56.849700Z",
     "iopub.status.idle": "2026-07-29T11:35:57.122958Z",
     "shell.execute_reply": "2026-07-29T11:35:57.120920Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method RE: pm\n",
      "                         eff    sd_eff     ci_low     ci_upp      w_fe      w_re\n",
      "PTB                61.000000  0.450000  60.118016  61.881984  0.057436  0.125857\n",
      "NMi                61.400000  1.100000  59.244040  63.555960  0.009612  0.059656\n",
      "NIMC               62.210000  0.300000  61.622011  62.797989  0.129230  0.143658\n",
      "KRISS              62.300000  0.450000  61.418016  63.181984  0.057436  0.125857\n",
      "LGC                62.340000  0.620000  61.124822  63.555178  0.030257  0.104850\n",
      "NRC                62.600000  0.750000  61.130027  64.069973  0.020677  0.090122\n",
      "IRMM               62.700000  0.260000  62.190409  63.209591  0.172052  0.147821\n",
      "NIST               62.840000  0.150000  62.546005  63.133995  0.516920  0.156980\n",
      "LNE                65.900000  1.350000  63.254049  68.545951  0.006382  0.045201\n",
      "fixed effect       62.583397  0.107846  62.334704  62.832090  1.000000       NaN\n",
      "random effect      62.407620  0.338030  61.628120  63.187119       NaN  1.000000\n",
      "fixed effect wls   62.583397  0.189889  62.145512  63.021282  1.000000       NaN\n",
      "random effect wls  62.407620  0.338030  61.628120  63.187120       NaN  1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/hostedtoolcache/Python/3.14.6/x64/lib/python3.14/site-packages/statsmodels/stats/meta_analysis.py:231: InvalidTestWarning: `use_t=True` requires `nobs` for each sample or `ci_func`. Using normal distribution for confidence interval of individual samples.\n",
      "  ci_low, ci_upp = self.conf_int_samples(alpha=alpha, use_t=use_t)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res2_PM = combine_effects(eff, var_eff, method_re=\"pm\", use_t=True, row_names=rownames)\n",
    "print(\"method RE:\", res2_PM.method_re)\n",
    "print(res2_PM.summary_frame())\n",
    "fig = res2_PM.plot_forest()\n",
    "fig.set_figheight(6)\n",
    "fig.set_figwidth(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meta-analysis of proportions\n",
    "\n",
    "In the following example the random effect variance tau is estimated to be zero. \n",
    "I then change two counts in the data, so the second example has random effects variance greater than zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:57.125453Z",
     "iopub.status.busy": "2026-07-29T11:35:57.125215Z",
     "iopub.status.idle": "2026-07-29T11:35:57.130360Z",
     "shell.execute_reply": "2026-07-29T11:35:57.129010Z"
    }
   },
   "outputs": [],
   "source": [
    "import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:57.132875Z",
     "iopub.status.busy": "2026-07-29T11:35:57.132615Z",
     "iopub.status.idle": "2026-07-29T11:35:57.141818Z",
     "shell.execute_reply": "2026-07-29T11:35:57.140382Z"
    }
   },
   "outputs": [],
   "source": [
    "ss = \"\"\"\\\n",
    "    study,nei,nci,e1i,c1i,e2i,c2i,e3i,c3i,e4i,c4i\n",
    "    1,19,22,16.0,20.0,11,12,4.0,8.0,4,3\n",
    "    2,34,35,22.0,22.0,18,12,15.0,8.0,15,6\n",
    "    3,72,68,44.0,40.0,21,15,10.0,3.0,3,0\n",
    "    4,22,20,19.0,12.0,14,5,5.0,4.0,2,3\n",
    "    5,70,32,62.0,27.0,42,13,26.0,6.0,15,5\n",
    "    6,183,94,130.0,65.0,80,33,47.0,14.0,30,11\n",
    "    7,26,50,24.0,30.0,13,18,5.0,10.0,3,9\n",
    "    8,61,55,51.0,44.0,37,30,19.0,19.0,11,15\n",
    "    9,36,25,30.0,17.0,23,12,13.0,4.0,10,4\n",
    "    10,45,35,43.0,35.0,19,14,8.0,4.0,6,0\n",
    "    11,246,208,169.0,139.0,106,76,67.0,42.0,51,35\n",
    "    12,386,141,279.0,97.0,170,46,97.0,21.0,73,8\n",
    "    13,59,32,56.0,30.0,34,17,21.0,9.0,20,7\n",
    "    14,45,15,42.0,10.0,18,3,9.0,1.0,9,1\n",
    "    15,14,18,14.0,18.0,13,14,12.0,13.0,9,12\n",
    "    16,26,19,21.0,15.0,12,10,6.0,4.0,5,1\n",
    "    17,74,75,,,42,40,,,23,30\"\"\"\n",
    "df3 = pd.read_csv(io.StringIO(ss))\n",
    "df_12y = df3[[\"e2i\", \"nei\", \"c2i\", \"nci\"]]\n",
    "# TODO: currently 1 is reference, switch labels\n",
    "count1, nobs1, count2, nobs2 = df_12y.values.T\n",
    "dta = df_12y.values.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:57.144220Z",
     "iopub.status.busy": "2026-07-29T11:35:57.143985Z",
     "iopub.status.idle": "2026-07-29T11:35:57.149182Z",
     "shell.execute_reply": "2026-07-29T11:35:57.147780Z"
    }
   },
   "outputs": [],
   "source": [
    "eff, var_eff = effectsize_2proportions(*dta, statistic=\"rd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:57.151627Z",
     "iopub.status.busy": "2026-07-29T11:35:57.151396Z",
     "iopub.status.idle": "2026-07-29T11:35:57.159569Z",
     "shell.execute_reply": "2026-07-29T11:35:57.158122Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.03349282,  0.18655462,  0.07107843,  0.38636364,  0.19375   ,\n",
       "         0.08609464,  0.14      ,  0.06110283,  0.15888889,  0.02222222,\n",
       "         0.06550969,  0.11417337,  0.04502119,  0.2       ,  0.15079365,\n",
       "        -0.06477733,  0.03423423]),\n",
       " array([0.02409958, 0.01376482, 0.00539777, 0.01989341, 0.01096641,\n",
       "        0.00376814, 0.01422338, 0.00842011, 0.01639261, 0.01227827,\n",
       "        0.00211165, 0.00219739, 0.01192067, 0.016     , 0.0143398 ,\n",
       "        0.02267994, 0.0066352 ]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eff, var_eff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:57.161992Z",
     "iopub.status.busy": "2026-07-29T11:35:57.161761Z",
     "iopub.status.idle": "2026-07-29T11:35:57.521319Z",
     "shell.execute_reply": "2026-07-29T11:35:57.519452Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method RE: iterated\n",
      "RE variance tau2: 0\n",
      "                        eff    sd_eff    ci_low    ci_upp      w_fe      w_re\n",
      "0                  0.033493  0.155240 -0.270773  0.337758  0.017454  0.017454\n",
      "1                  0.186555  0.117324 -0.043395  0.416505  0.030559  0.030559\n",
      "2                  0.071078  0.073470 -0.072919  0.215076  0.077928  0.077928\n",
      "3                  0.386364  0.141044  0.109922  0.662805  0.021145  0.021145\n",
      "4                  0.193750  0.104721 -0.011499  0.398999  0.038357  0.038357\n",
      "5                  0.086095  0.061385 -0.034218  0.206407  0.111630  0.111630\n",
      "6                  0.140000  0.119262 -0.093749  0.373749  0.029574  0.029574\n",
      "7                  0.061103  0.091761 -0.118746  0.240951  0.049956  0.049956\n",
      "8                  0.158889  0.128034 -0.092052  0.409830  0.025660  0.025660\n",
      "9                  0.022222  0.110807 -0.194956  0.239401  0.034259  0.034259\n",
      "10                 0.065510  0.045953 -0.024556  0.155575  0.199199  0.199199\n",
      "11                 0.114173  0.046876  0.022297  0.206049  0.191426  0.191426\n",
      "12                 0.045021  0.109182 -0.168971  0.259014  0.035286  0.035286\n",
      "13                 0.200000  0.126491 -0.047918  0.447918  0.026290  0.026290\n",
      "14                 0.150794  0.119749 -0.083910  0.385497  0.029334  0.029334\n",
      "15                -0.064777  0.150599 -0.359945  0.230390  0.018547  0.018547\n",
      "16                 0.034234  0.081457 -0.125418  0.193887  0.063395  0.063395\n",
      "fixed effect       0.096212  0.020509  0.056014  0.136410  1.000000       NaN\n",
      "random effect      0.096212  0.020509  0.056014  0.136410       NaN  1.000000\n",
      "fixed effect wls   0.096212  0.016521  0.063831  0.128593  1.000000       NaN\n",
      "random effect wls  0.096212  0.016521  0.063831  0.128593       NaN  1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res5 = combine_effects(\n",
    "    eff, var_eff, method_re=\"iterated\", use_t=False\n",
    ")  # , row_names=rownames)\n",
    "res5_df = res5.summary_frame()\n",
    "print(\"method RE:\", res5.method_re)\n",
    "print(\"RE variance tau2:\", res5.tau2)\n",
    "print(res5.summary_frame())\n",
    "fig = res5.plot_forest()\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### changing data to have positive random effects variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:57.523965Z",
     "iopub.status.busy": "2026-07-29T11:35:57.523694Z",
     "iopub.status.idle": "2026-07-29T11:35:57.532807Z",
     "shell.execute_reply": "2026-07-29T11:35:57.531280Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 18,  19,  12,  22],\n",
       "       [ 22,  34,  12,  35],\n",
       "       [ 21,  72,  15,  68],\n",
       "       [ 14,  22,   5,  20],\n",
       "       [ 42,  70,  13,  32],\n",
       "       [ 80, 183,  33,  94],\n",
       "       [ 13,  26,  18,  50],\n",
       "       [ 37,  61,  30,  55],\n",
       "       [ 23,  36,  12,  25],\n",
       "       [ 19,  45,  14,  35],\n",
       "       [106, 246,  76, 208],\n",
       "       [170, 386,  46, 141],\n",
       "       [ 34,  59,  17,  32],\n",
       "       [ 18,  45,   3,  15],\n",
       "       [ 13,  14,  14,  18],\n",
       "       [ 12,  26,  10,  19],\n",
       "       [ 42,  74,  40,  75]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dta_c = dta.copy()\n",
    "dta_c.T[0, 0] = 18\n",
    "dta_c.T[1, 0] = 22\n",
    "dta_c.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:57.535497Z",
     "iopub.status.busy": "2026-07-29T11:35:57.535241Z",
     "iopub.status.idle": "2026-07-29T11:35:57.890541Z",
     "shell.execute_reply": "2026-07-29T11:35:57.889641Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method RE: iterated\n",
      "                        eff    sd_eff    ci_low    ci_upp      w_fe      w_re\n",
      "0                  0.401914  0.117873  0.170887  0.632940  0.029850  0.038415\n",
      "1                  0.304202  0.114692  0.079410  0.528993  0.031529  0.040258\n",
      "2                  0.071078  0.073470 -0.072919  0.215076  0.076834  0.081017\n",
      "3                  0.386364  0.141044  0.109922  0.662805  0.020848  0.028013\n",
      "4                  0.193750  0.104721 -0.011499  0.398999  0.037818  0.046915\n",
      "5                  0.086095  0.061385 -0.034218  0.206407  0.110063  0.102907\n",
      "6                  0.140000  0.119262 -0.093749  0.373749  0.029159  0.037647\n",
      "7                  0.061103  0.091761 -0.118746  0.240951  0.049255  0.058097\n",
      "8                  0.158889  0.128034 -0.092052  0.409830  0.025300  0.033270\n",
      "9                  0.022222  0.110807 -0.194956  0.239401  0.033778  0.042683\n",
      "10                 0.065510  0.045953 -0.024556  0.155575  0.196403  0.141871\n",
      "11                 0.114173  0.046876  0.022297  0.206049  0.188739  0.139144\n",
      "12                 0.045021  0.109182 -0.168971  0.259014  0.034791  0.043759\n",
      "13                 0.200000  0.126491 -0.047918  0.447918  0.025921  0.033985\n",
      "14                 0.150794  0.119749 -0.083910  0.385497  0.028922  0.037383\n",
      "15                -0.064777  0.150599 -0.359945  0.230390  0.018286  0.024884\n",
      "16                 0.034234  0.081457 -0.125418  0.193887  0.062505  0.069751\n",
      "fixed effect       0.110252  0.020365  0.070337  0.150167  1.000000       NaN\n",
      "random effect      0.117633  0.024913  0.068804  0.166463       NaN  1.000000\n",
      "fixed effect wls   0.110252  0.022289  0.066567  0.153937  1.000000       NaN\n",
      "random effect wls  0.117633  0.024913  0.068804  0.166463       NaN  1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eff, var_eff = effectsize_2proportions(*dta_c, statistic=\"rd\")\n",
    "res5 = combine_effects(\n",
    "    eff, var_eff, method_re=\"iterated\", use_t=False\n",
    ")  # , row_names=rownames)\n",
    "res5_df = res5.summary_frame()\n",
    "print(\"method RE:\", res5.method_re)\n",
    "print(res5.summary_frame())\n",
    "fig = res5.plot_forest()\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:57.895932Z",
     "iopub.status.busy": "2026-07-29T11:35:57.895632Z",
     "iopub.status.idle": "2026-07-29T11:35:58.253793Z",
     "shell.execute_reply": "2026-07-29T11:35:58.252645Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method RE: chi2\n",
      "                        eff    sd_eff    ci_low    ci_upp      w_fe      w_re\n",
      "0                  0.401914  0.117873  0.170887  0.632940  0.029850  0.036114\n",
      "1                  0.304202  0.114692  0.079410  0.528993  0.031529  0.037940\n",
      "2                  0.071078  0.073470 -0.072919  0.215076  0.076834  0.080779\n",
      "3                  0.386364  0.141044  0.109922  0.662805  0.020848  0.025973\n",
      "4                  0.193750  0.104721 -0.011499  0.398999  0.037818  0.044614\n",
      "5                  0.086095  0.061385 -0.034218  0.206407  0.110063  0.105901\n",
      "6                  0.140000  0.119262 -0.093749  0.373749  0.029159  0.035356\n",
      "7                  0.061103  0.091761 -0.118746  0.240951  0.049255  0.056098\n",
      "8                  0.158889  0.128034 -0.092052  0.409830  0.025300  0.031063\n",
      "9                  0.022222  0.110807 -0.194956  0.239401  0.033778  0.040357\n",
      "10                 0.065510  0.045953 -0.024556  0.155575  0.196403  0.154854\n",
      "11                 0.114173  0.046876  0.022297  0.206049  0.188739  0.151236\n",
      "12                 0.045021  0.109182 -0.168971  0.259014  0.034791  0.041435\n",
      "13                 0.200000  0.126491 -0.047918  0.447918  0.025921  0.031761\n",
      "14                 0.150794  0.119749 -0.083910  0.385497  0.028922  0.035095\n",
      "15                -0.064777  0.150599 -0.359945  0.230390  0.018286  0.022976\n",
      "16                 0.034234  0.081457 -0.125418  0.193887  0.062505  0.068449\n",
      "fixed effect       0.110252  0.020365  0.070337  0.150167  1.000000       NaN\n",
      "random effect      0.115580  0.023557  0.069410  0.161751       NaN  1.000000\n",
      "fixed effect wls   0.110252  0.022289  0.066567  0.153937  1.000000       NaN\n",
      "random effect wls  0.115580  0.024241  0.068068  0.163093       NaN  1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res5 = combine_effects(eff, var_eff, method_re=\"chi2\", use_t=False)\n",
    "res5_df = res5.summary_frame()\n",
    "print(\"method RE:\", res5.method_re)\n",
    "print(res5.summary_frame())\n",
    "fig = res5.plot_forest()\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replicate fixed effect analysis using GLM with var_weights\n",
    "\n",
    "`combine_effects` computes weighted average estimates which can be replicated using GLM with var_weights or with WLS.\n",
    "The `scale` option in `GLM.fit` can be used to replicate fixed meta-analysis with fixed and with HKSJ/WLS scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.256448Z",
     "iopub.status.busy": "2026-07-29T11:35:58.256192Z",
     "iopub.status.idle": "2026-07-29T11:35:58.268031Z",
     "shell.execute_reply": "2026-07-29T11:35:58.267314Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        eff    sd_eff    ci_low    ci_upp  w_fe  w_re\n",
      "fixed effect       0.428037  0.090287  0.251076  0.604997   1.0   NaN\n",
      "random effect      0.429520  0.091377  0.250425  0.608615   NaN   1.0\n",
      "fixed effect wls   0.428037  0.090798  0.250076  0.605997   1.0   NaN\n",
      "random effect wls  0.429520  0.091595  0.249997  0.609044   NaN   1.0\n"
     ]
    }
   ],
   "source": [
    "eff, var_eff = effectsize_2proportions(*dta_c, statistic=\"or\")\n",
    "res = combine_effects(eff, var_eff, method_re=\"chi2\", use_t=False)\n",
    "res_frame = res.summary_frame()\n",
    "print(res_frame.iloc[-4:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to fix scale=1 in order to replicate standard errors for the usual meta-analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.271787Z",
     "iopub.status.busy": "2026-07-29T11:35:58.271528Z",
     "iopub.status.idle": "2026-07-29T11:35:58.283937Z",
     "shell.execute_reply": "2026-07-29T11:35:58.282464Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.4280      0.090      4.741      0.000       0.251       0.605\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "weights = 1 / var_eff\n",
    "mod_glm = GLM(eff, np.ones(len(eff)), var_weights=weights)\n",
    "res_glm = mod_glm.fit(scale=1.0)\n",
    "print(res_glm.summary().tables[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.286286Z",
     "iopub.status.busy": "2026-07-29T11:35:58.286046Z",
     "iopub.status.idle": "2026-07-29T11:35:58.295308Z",
     "shell.execute_reply": "2026-07-29T11:35:58.293915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(1.), array([[-5.55111512e-17,  0.00000000e+00]]))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check results\n",
    "res_glm.scale, res_glm.conf_int() - res_frame.loc[\n",
    "    \"fixed effect\", [\"ci_low\", \"ci_upp\"]\n",
    "].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using HKSJ variance adjustment in meta-analysis is equivalent to estimating the scale using pearson chi2, which is also the default for the gaussian family."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.297503Z",
     "iopub.status.busy": "2026-07-29T11:35:58.297267Z",
     "iopub.status.idle": "2026-07-29T11:35:58.308997Z",
     "shell.execute_reply": "2026-07-29T11:35:58.307482Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.4280      0.091      4.714      0.000       0.250       0.606\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "res_glm = mod_glm.fit(scale=\"x2\")\n",
    "print(res_glm.summary().tables[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.311376Z",
     "iopub.status.busy": "2026-07-29T11:35:58.311136Z",
     "iopub.status.idle": "2026-07-29T11:35:58.320556Z",
     "shell.execute_reply": "2026-07-29T11:35:58.319042Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(1.0113358914264383), array([[-0.00100017,  0.00100017]]))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check results\n",
    "res_glm.scale, res_glm.conf_int() - res_frame.loc[\n",
    "    \"fixed effect\", [\"ci_low\", \"ci_upp\"]\n",
    "].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mantel-Hanszel odds-ratio using contingency tables\n",
    "\n",
    "The fixed effect for the log-odds-ratio using the Mantel-Hanszel can be directly computed using StratifiedTable.\n",
    "\n",
    "We need to create a 2 x 2 x k contingency table to be used with `StratifiedTable`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.322813Z",
     "iopub.status.busy": "2026-07-29T11:35:58.322543Z",
     "iopub.status.idle": "2026-07-29T11:35:58.331237Z",
     "shell.execute_reply": "2026-07-29T11:35:58.329831Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18,  1],\n",
       "       [12, 10]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t, nt, c, nc = dta_c\n",
    "counts = np.column_stack([t, nt - t, c, nc - c])\n",
    "ctables = counts.T.reshape(2, 2, -1)\n",
    "ctables[:, :, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.333432Z",
     "iopub.status.busy": "2026-07-29T11:35:58.333197Z",
     "iopub.status.idle": "2026-07-29T11:35:58.340678Z",
     "shell.execute_reply": "2026-07-29T11:35:58.339311Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18,  1, 12, 10])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.343245Z",
     "iopub.status.busy": "2026-07-29T11:35:58.343016Z",
     "iopub.status.idle": "2026-07-29T11:35:58.350448Z",
     "shell.execute_reply": "2026-07-29T11:35:58.348995Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18, 19, 12, 22])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dta_c.T[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.352623Z",
     "iopub.status.busy": "2026-07-29T11:35:58.352397Z",
     "iopub.status.idle": "2026-07-29T11:35:58.380149Z",
     "shell.execute_reply": "2026-07-29T11:35:58.378501Z"
    }
   },
   "outputs": [],
   "source": [
    "import statsmodels.stats.api as smstats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.382682Z",
     "iopub.status.busy": "2026-07-29T11:35:58.382445Z",
     "iopub.status.idle": "2026-07-29T11:35:58.387664Z",
     "shell.execute_reply": "2026-07-29T11:35:58.386234Z"
    }
   },
   "outputs": [],
   "source": [
    "st = smstats.StratifiedTable(ctables.astype(np.float64))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "compare pooled log-odds-ratio and standard error to R meta package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.390082Z",
     "iopub.status.busy": "2026-07-29T11:35:58.389851Z",
     "iopub.status.idle": "2026-07-29T11:35:58.397406Z",
     "shell.execute_reply": "2026-07-29T11:35:58.395993Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.4428186730553187), np.float64(-2.220446049250313e-16))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.logodds_pooled, st.logodds_pooled - 0.4428186730553189  # R meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.399874Z",
     "iopub.status.busy": "2026-07-29T11:35:58.399606Z",
     "iopub.status.idle": "2026-07-29T11:35:58.407025Z",
     "shell.execute_reply": "2026-07-29T11:35:58.405542Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.08928560091027186), np.float64(0.0))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.logodds_pooled_se, st.logodds_pooled_se - 0.08928560091027186  # R meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.409158Z",
     "iopub.status.busy": "2026-07-29T11:35:58.408929Z",
     "iopub.status.idle": "2026-07-29T11:35:58.416872Z",
     "shell.execute_reply": "2026-07-29T11:35:58.415495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.2678221109331691), np.float64(0.6178152351774683))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.logodds_pooled_confint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.419167Z",
     "iopub.status.busy": "2026-07-29T11:35:58.418956Z",
     "iopub.status.idle": "2026-07-29T11:35:58.424958Z",
     "shell.execute_reply": "2026-07-29T11:35:58.423492Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pvalue      0.34496419319878724\n",
      "statistic   17.64707987033203\n"
     ]
    }
   ],
   "source": [
    "print(st.test_equal_odds())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.427167Z",
     "iopub.status.busy": "2026-07-29T11:35:58.426939Z",
     "iopub.status.idle": "2026-07-29T11:35:58.433142Z",
     "shell.execute_reply": "2026-07-29T11:35:58.431685Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pvalue      6.615053645964153e-07\n",
      "statistic   24.724136624311814\n"
     ]
    }
   ],
   "source": [
    "print(st.test_null_odds())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "check conversion to stratified contingency table\n",
    "\n",
    "Row sums of each table are the sample sizes for treatment and control experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.435194Z",
     "iopub.status.busy": "2026-07-29T11:35:58.434967Z",
     "iopub.status.idle": "2026-07-29T11:35:58.441546Z",
     "shell.execute_reply": "2026-07-29T11:35:58.440882Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 19,  34,  72,  22,  70, 183,  26,  61,  36,  45, 246, 386,  59,\n",
       "         45,  14,  26,  74],\n",
       "       [ 22,  35,  68,  20,  32,  94,  50,  55,  25,  35, 208, 141,  32,\n",
       "         15,  18,  19,  75]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctables.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.444252Z",
     "iopub.status.busy": "2026-07-29T11:35:58.444023Z",
     "iopub.status.idle": "2026-07-29T11:35:58.448908Z",
     "shell.execute_reply": "2026-07-29T11:35:58.448276Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 19,  34,  72,  22,  70, 183,  26,  61,  36,  45, 246, 386,  59,\n",
       "         45,  14,  26,  74]),\n",
       " array([ 22,  35,  68,  20,  32,  94,  50,  55,  25,  35, 208, 141,  32,\n",
       "         15,  18,  19,  75]))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nt, nc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Results from R meta package**\n",
    "\n",
    "```\n",
    "> res_mb_hk = metabin(e2i, nei, c2i, nci, data=dat2, sm=\"OR\", Q.Cochrane=FALSE, method=\"MH\", method.tau=\"DL\", hakn=FALSE, backtransf=FALSE)\n",
    "> res_mb_hk\n",
    "     logOR            95%-CI %W(fixed) %W(random)\n",
    "1   2.7081 [ 0.5265; 4.8896]       0.3        0.7\n",
    "2   1.2567 [ 0.2658; 2.2476]       2.1        3.2\n",
    "3   0.3749 [-0.3911; 1.1410]       5.4        5.4\n",
    "4   1.6582 [ 0.3245; 2.9920]       0.9        1.8\n",
    "5   0.7850 [-0.0673; 1.6372]       3.5        4.4\n",
    "6   0.3617 [-0.1528; 0.8762]      12.1       11.8\n",
    "7   0.5754 [-0.3861; 1.5368]       3.0        3.4\n",
    "8   0.2505 [-0.4881; 0.9892]       6.1        5.8\n",
    "9   0.6506 [-0.3877; 1.6889]       2.5        3.0\n",
    "10  0.0918 [-0.8067; 0.9903]       4.5        3.9\n",
    "11  0.2739 [-0.1047; 0.6525]      23.1       21.4\n",
    "12  0.4858 [ 0.0804; 0.8911]      18.6       18.8\n",
    "13  0.1823 [-0.6830; 1.0476]       4.6        4.2\n",
    "14  0.9808 [-0.4178; 2.3795]       1.3        1.6\n",
    "15  1.3122 [-1.0055; 3.6299]       0.4        0.6\n",
    "16 -0.2595 [-1.4450; 0.9260]       3.1        2.3\n",
    "17  0.1384 [-0.5076; 0.7844]       8.5        7.6\n",
    "\n",
    "Number of studies combined: k = 17\n",
    "\n",
    "                      logOR           95%-CI    z  p-value\n",
    "Fixed effect model   0.4428 [0.2678; 0.6178] 4.96 < 0.0001\n",
    "Random effects model 0.4295 [0.2504; 0.6086] 4.70 < 0.0001\n",
    "\n",
    "Quantifying heterogeneity:\n",
    " tau^2 = 0.0017 [0.0000; 0.4589]; tau = 0.0410 [0.0000; 0.6774];\n",
    " I^2 = 1.1% [0.0%; 51.6%]; H = 1.01 [1.00; 1.44]\n",
    "\n",
    "Test of heterogeneity:\n",
    "     Q d.f. p-value\n",
    " 16.18   16  0.4404\n",
    "\n",
    "Details on meta-analytical method:\n",
    "- Mantel-Haenszel method\n",
    "- DerSimonian-Laird estimator for tau^2\n",
    "- Jackson method for confidence interval of tau^2 and tau\n",
    "\n",
    "> res_mb_hk$TE.fixed\n",
    "[1] 0.4428186730553189\n",
    "> res_mb_hk$seTE.fixed\n",
    "[1] 0.08928560091027186\n",
    "> c(res_mb_hk$lower.fixed, res_mb_hk$upper.fixed)\n",
    "[1] 0.2678221109331694 0.6178152351774684\n",
    " \n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T11:35:58.452990Z",
     "iopub.status.busy": "2026-07-29T11:35:58.452763Z",
     "iopub.status.idle": "2026-07-29T11:35:58.463397Z",
     "shell.execute_reply": "2026-07-29T11:35:58.462426Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Estimate   LCB    UCB \n",
      "-----------------------------------------\n",
      "Pooled odds           1.557   1.307 1.855\n",
      "Pooled log odds       0.443   0.268 0.618\n",
      "Pooled risk ratio     1.270              \n",
      "                                         \n",
      "                 Statistic P-value     \n",
      "---------------------------------------\n",
      "Test of OR=1        24.724   0.000     \n",
      "Test constant OR    17.647   0.345     \n",
      "                         \n",
      "-------------------------\n",
      "Number of tables   17    \n",
      "Min n              32    \n",
      "Max n             527    \n",
      "Avg n             139    \n",
      "Total n          2362    \n",
      "-------------------------\n"
     ]
    }
   ],
   "source": [
    "print(st.summary())"
   ]
  }
 ],
 "metadata": {
  "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": "3.14.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}