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help parmhet_hettest_opts                                        (Roger Newson)
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Heterogeneity-test options for parmhet and parmiv


    options                   Description
    -------------------------------------------------------------------------
    chi2het(newvarname)       Heterogeneity chi-squared statistic variable
    dfhet(newvarname)         Heterogeneity degrees of freedom variable
    i2het(newvarname)         Heterogeneity I-squared statistic variable
    tau2het(newvarname)       Heterogeneity tau-squared statistic variable
    fhet(newvarname)          Heterogeneity F-statistic variable
    resdfhet(newvarname)      Heterogeneity residual degrees of freedom
                                variable
    phet(newvarname)          Heterogeneity P-value variable
    -------------------------------------------------------------------------


Description

    These options specify the names of generated variables, containing
    heterogeneity-test statistics for the input dataset, or for the by-group
    if the by() option is specified.  If specified for parmhet, these options
    refer to generated variables in the parmhet resultsset (see help for
    parmhet_resultsset), and cause the variable with the same name as the
    option to be renamed to the name specified by the option.  If specified
    for parmiv, these options cause a new variable of the specified name to
    be added to the existing dataset in memory.


Options

    chi2het(newvarname) specifies the name of an output variable containing
        the heterogeneity chi-squared statistic defined by Cochrane (1954).

    dfhet(newvarname) specifies the name of an output variable containing the
        degrees of freedom for the heterogeneity chi-squared statistic
        defined by Cochrane (1954).

    i2het(newvarname) specifies the name of an output variable containing the
        heterogeneity I-squared statistic defined by Higgins and Thompson
        (2002).  This statistic is expressed on a percentage scale from 0 to
        100, and denotes the percentage excess of the heterogeneity
        chi-squared statistic, compared to its mean value under the null
        hypothesis of no heterogeneity, specified by its degrees of freedom.
        If the chi-squared statistic is less than its degrees of freedom,
        then the I-squared statistic is zero.

    tau2het(newvarname) specifies the name of an output variable containing
        the heterogeneity tau-squared statistic defined by Higgins and
        Thompson (2002).  The tau-squared statistic is an estimate of the
        variance of the true population values of the estimated parameters,
        in the meta-population from which these populations are sampled.  It
        is expressed in squared units of the input parameter estimates, or in
        squared log units of the input parameter estimates, if the eform
        option is specified (see help for parmhet_basic_opts).  If the
        chi-squared statistic is less than its degrees of freedom, then the
        tau-squared statistic is zero.

    fhet(newvarname) specifies the name of an output variable containing the
        heterogeneity F-statistic defined by Welch (1951) and popularized by
        Cochrane (1954).  This variable is only calculated if the user
        specifies an input degrees of freedom variable, in addition to the
        input estimate and standard error variables.  If an input degrees of
        freedom variable is not provided, then the fhet() option is ignored.

    resdfhet(newvarname) specifies the name of an output variable containing
        the residual (or denominator) degrees of freedom for the
        heterogeneity F-statistic output to the fhet() variable.  This
        denominator degrees of freedom variable may have non-integer values,
        and is used, together with the numerator degrees of freedom output to
        the dfhet() variable, to calculate a P-value (output to the phet()
        variable) for the heterogeneity F-statistic (output to the fhet()
        variable).  The resdfhet() variable is only calculated if the user
        specifies an input degrees of freedom variable, in addition to the
        input estimate and standard error variables.  If an input degrees of
        freedom variable is not provided, then the resdfhet() option is
        ignored.

    phet(newvarname) specifies the name of an output variable containing the
        heterogeneity P-value.  If an input degrees of freedom variable is
        specified, then this P-value is calculated using the F-statistic
        output to the fhet() variable, with the numerator degrees of freedom
        output to the dfhet() variable and the denominator degrees of freedom
        output to the resdfhet() variable.  If an input degrees of freedom
        variable is not specified, then this P-value is calculated using the
        chi-squared statistic output to the chi2het() variable, with the
        degrees of freedom output to the dfhet() variable.


Author

    Roger Newson, National Heart and Lung Institute, Imperial College London,
    UK.  Email: r.newson@imperial.ac.uk


References

    Cochrane, W. G.  1954.  The combination of estimates from different
        experiments.  Biometrics 10(1): 101-129.

    Higgins, J. P. T. and Thompson, S. G.  2002.  Quantifying heterogeneity
        in a meta-analysis.  Statistics in Medicine 21(11): 1539-1558.

    Welch, B. L.  1951.  On the comparison of several mean values: an
        alternative approach.  Biometrika 36(3/4): 330-336.


Also see

     Manual: [R] meta, [R] test
    On-line: help for parmhet, parmiv, parmhet_basic_opts,
             parmhet_resultsset_opts, parmhet_resultsset
             help for test
             help for parmest, parmby, parmcip, metaparm, metan if installed