help parmhet_hettest_opts(Roger Newson)

Heterogeneity-test options for parmhet and parmiv

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