class_docstr: Analyses that can be performed on a 2x2 contingency table.
  
  Parameters
  ----------
  table : array_like
      A 2x2 contingency table
  shift_zeros : bool
      If true, 0.5 is added to all cells of the table if any cell is
      equal to zero.
  
  Notes
  -----
  The inference procedures used here are all based on a sampling
  model in which the units are independent and identically
  distributed, with each unit being classified with respect to two
  categorical variables.
  
  Note that for the risk ratio, the analysis is not symmetric with
  respect to the rows and columns of the contingency table.  The two
  rows define population subgroups, column 0 is the number of
  'events', and column 1 is the number of 'non-events'.
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docstr: P-value for a hypothesis test about the log odds ratio.
  
  Parameters
  ----------
  null : float
      The null value of the log odds ratio.
docstr_tok: ["P-value","for","a","hypothesis","test","about","the","log","odds","ratio",".","Parameters","--","--","--","--","--","null",":","float","The","null","value","of","the","log","odds","ratio","."]
code: def log_oddsratio_pvalue(self, null=0):
      """
      P-value for a hypothesis test about the log odds ratio.
  
      Parameters
      ----------
      null : float
          The null value of the log odds ratio.
      """
  
      zscore = (self.log_oddsratio - null) / self.log_oddsratio_se
      pvalue = 2 * stats.norm.cdf(-np.abs(zscore))
      return pvalue
  
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 Table2x2 obj's underlying member method code with example usage: obj.log_oddsratio_pvalue(null) and returns: pvalue