code: def effectsize_smd(mean1, sd1, nobs1, mean2, sd2, nobs2):
      """effect sizes for mean difference for use in meta-analysis
  
      mean1, sd1, nobs1 are for treatment
      mean2, sd2, nobs2 are for control
  
      Effect sizes are computed for the mean difference ``mean1 - mean2``
      standardized by an estimate of the within variance.
  
      This does not have option yet.
      It uses standardized mean difference with bias correction as effect size.
  
      This currently does not use np.asarray, all computations are possible in
      pandas.
  
      Parameters
      ----------
      mean1 : array
          mean of second sample, treatment groups
      sd1 : array
          standard deviation of residuals in treatment groups, within
      nobs1 : array
          number of observations in treatment groups
      mean2, sd2, nobs2 : arrays
          mean, standard deviation and number of observations of control groups
  
      Returns
      -------
      smd_bc : array
          bias corrected estimate of standardized mean difference
      var_smdbc : array
          estimate of variance of smd_bc
  
      Notes
      -----
      Status: API will still change. This is currently intended for support of
      meta-analysis.
  
      References
      ----------
      Borenstein, Michael. 2009. Introduction to Meta-Analysis.
          Chichester: Wiley.
  
      Chen, Ding-Geng, and Karl E. Peace. 2013. Applied Meta-Analysis with R.
          Chapman & Hall/CRC Biostatistics Series.
          Boca Raton: CRC Press/Taylor & Francis Group.
  
      """
      # TODO: not used yet, design and options ?
      # k = len(mean1)
      # if row_names is None:
      #    row_names = list(range(k))
      # crit = stats.norm.isf(alpha / 2)
      # var_diff_uneq = sd1**2 / nobs1 + sd2**2 / nobs2
      var_diff = (sd1**2 * (nobs1 - 1) + sd2**2 * (nobs2 - 1)) / (
          nobs1 + nobs2 - 2
      )
      sd_diff = np.sqrt(var_diff)
      nobs = nobs1 + nobs2
      bias_correction = 1 - 3 / (4 * nobs - 9)
      smd = (mean1 - mean2) / sd_diff
      smd_bc = bias_correction * smd
      var_smdbc = nobs / nobs1 / nobs2 + smd_bc**2 / 2 / (nobs - 3.94)
      return smd_bc, var_smdbc
  
file_path: statsmodels/statsmodels/stats/meta_analysis.py
imports: import numpy
  import pandas
  from scipy import stats
  from statsmodels.stats.base import HolderTuple
label_desc: Use image node_id 1 for calling a global function with example usage: effectsize_smd(mean1, sd1, nobs1, mean2, sd2, nobs2) and returns: smd_bc,  var_smdbc