-------------------------------------------------------------------------------
help for hetred
-------------------------------------------------------------------------------

Heterogeneity reducing algorithms

    hetred #logEffect #SElogEffect , i2h(#) i2l(#) ID(varname)
              [random tuples max]

    where

    #logEffect is the ln of the effect size estimate
    #SElogEffect is the standard error of ln of the effect size
              estimate



Description

    hetred reduces the statistical heterogeneity of meta-analysis as
          measured by I-squared. It uses 2 differnt algorithms. A
          sequential one (default) and a combinatorial one (tuples
          option).  For further explanation please see Reference
          section below. Using the combinatorial algorithm can demand
          extreme CPU resources in case where more than 6-7 studies
          have to be dropped.  Always run the sequential algorithm
          (default option) to see how many studies have been
          ommitted. The difference between the two is that the
          combinatorial one might find a better combination ommitting
          fewer studies.


 
Options

    i2h is the level of initial I-squared we want to evaluate, e.g.
              50
    i2l is the level under which we want to drop the I-squared , e.g.
              25
    ID is a variable that uniquely identifies the studies
    random uses the random effects model to synthesize the data.
              Fixed effects model is used by default.
    tuples uses the combinatorial algorithm as well. Drops 2, 3, etc
              studies together at each step till i2l level is
              reached.
    max returns the result that drops I-squared below i2l but has the
              largest possible heterogeneity.


Examples
        If I-squared >=75% then try to drop it under 25%
     . hetred lnES selnES, i2h(75) i2l(25) ID(study)

        The same as above but using random effects model (affects only the summ
> ary estimate and respective CIs)
     . hetred lnES selnES, i2h(75) i2l(25) ID(study) random

        Using combinatorial algorithm as well
     . hetred lnES selnES, i2h(75) i2l(25) ID(study) random tuples

        Asking for the maximum possible I-squared below the i2l (25%)
     . hetred lnES selnES, i2h(75) i2l(25) ID(study) max


Author

 
    Nikolaos A Patsopoulos, Department of Hygiene and Epidemiology University
    of Ioannina School of Medicine


Reference

    Patsopoulos NA, Evangelou E, Ioannidis JPA. Sensitivity of between-study
    heterogeneity in meta-analysis: Proposed metrics and empirical
    evaluation, Submitted

Support

    npatsop@cc.uoi.gr
    npatsop@gmail.com

Also see

On-line:  help for metan