class_docstr: Analyses for a collection of 2x2 contingency tables.
  
  Such a collection may arise by stratifying a single 2x2 table with
  respect to another factor.  This class implements the
  'Cochran-Mantel-Haenszel' and 'Breslow-Day' procedures for
  analyzing collections of 2x2 contingency tables.
  
  Parameters
  ----------
  tables : list or ndarray
      Either a list containing several 2x2 contingency tables, or
      a 2x2xk ndarray in which each slice along the third axis is a
      2x2 contingency table.
  
  Notes
  -----
  This results are based on a sampling model in which the units are
  independent both within and between strata.
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docstr: Estimated standard error of the pooled log odds ratio
  
  References
  ----------
  J. Robins, N. Breslow, S. Greenland. "Estimators of the
  Mantel-Haenszel Variance Consistent in Both Sparse Data and
  Large-Strata Limiting Models." Biometrics 42, no. 2 (1986): 311-23.
docstr_tok: ["Estimated","standard","error","of","the","pooled","log","odds","ratio","References","--","--","--","--","--","J.","Robins",",","N.","Breslow",",","S.","Greenland",".","``","Estimators","of","the","Mantel-Haenszel","Variance","Consistent","in","Both","Sparse","Data","and","Large-Strata","Limiting","Models",".","''","Biometrics","42",",","no",".","2","(","1986",")",":","311-23","."]
code: def logodds_pooled_se(self):
      """
      Estimated standard error of the pooled log odds ratio
  
      References
      ----------
      J. Robins, N. Breslow, S. Greenland. "Estimators of the
      Mantel-Haenszel Variance Consistent in Both Sparse Data and
      Large-Strata Limiting Models." Biometrics 42, no. 2 (1986): 311-23.
      """
  
      adns = np.sum(self._ad / self._n)
      bcns = np.sum(self._bc / self._n)
      lor_va = np.sum(self._apd * self._ad / self._n**2) / adns**2
      mid = self._apd * self._bc / self._n**2
      mid += (1 - self._apd / self._n) * self._ad / self._n
      mid = np.sum(mid)
      mid /= adns * bcns
      lor_va += mid
      lor_va += (
          np.sum((1 - self._apd / self._n) * self._bc / self._n)
          / bcns**2
      )
      lor_va /= 2
      lor_se = np.sqrt(lor_va)
      return lor_se
  
imports: import warnings
  import numpy
  import pandas
  from scipy import stats
  from statsmodels import iolib
  from statsmodels.tools import sm_exceptions
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 7 for calling the StratifiedTable obj's underlying member method code with example usage: obj.logodds_pooled_se() and returns: lor_se