help parmhet_basic_opts(Roger Newson)

Basic options for parmhet and parmiv

by(varlist) Variables specifying by-groups

eform Estimates and confidence limits exponentiated

float Numeric output variables of type float or less

dfcombine(combination_rule) Rule for combining degrees of freedom

ivweight(newvarname) Name of generated inverse-variance weight variable

sweight(newvarname) Name of generated semi-weight variable

sstderr(newvarname) Name of generated semi-weight-based standard error variable

where combination_rule is

welch | constant

Description

These options are used in a similar way by parmhet and parmiv.

Options

by(varlist) specifies a list of existing by-variables in the input dataset.
The heterogeneity-test statistics are computed within by-groups if by() is specified.
If by() is not specified, then the test statistics are computed for the whole input dataset.
The output dataset (or resultsset) created by parmhet
has one observation per by-group, if by() is specified,
and only one observation otherwise.

eform indicates that the input estimates are exponentiated,
and that the input standard errors are multiplied by the exponentiated estimate.
eform is used when the user chooses to calculate heterogeneity-test statistics
for the logs of the input parameter estimates,
as is usual when the input parameters are odds ratios, risk ratios, or geometric mean ratios.
Otherwise, heterogeneity-test statistics are calculated for the input parameters themselves.

float specifies that the numeric output variables will be created as type float or below.
If float is unset, then the numeric output variables are created as type double.
Note that all generated variables are compressed as much as possible without loss of information,
whether or not float is specified.

dfcombine(combination_rule) specifies a rule for combining the degrees of freedom of the input parameters
to define the denominator degrees of freedom for the F-test statistic,
if a degrees of freedom variable is specified by the user.
If dfcombine(welch) is specified, then parmhet and parmiv use the formula of Welch (1951),
popularized by Cochrane (1954),
to calculate the F-statistic and its denominator degrees of freedom.
If dfcombine(constant) is specified,
then parmhet and parmiv check that the input degrees of freedom
are constant (or constant within by-groups if by(varlist) is specified),
and then sets the denominator degrees of freedom to the constant input degrees of freedom,
and calculates the heterogeneity F-statistic by dividing the heterogeneity chi-squared statistic
by the heterogeneity degrees of freedom.
dfcombine() is set to welch by default,
but is ignored if a degrees of freedom variable is not specified.
The option dfcombine(constant) is useful if the input parameters are uncorrelated parameters
belonging to the same model estimation with pooled degrees of freedom,
such as group means estimated using the regress command
with group membership indicators as X-variables,
using the noconst option,
and the user uses parmhet or parmiv to test for heterogeneity between groups.
In these circumstances, using regress without the robust option
and using dfcombine(constant) with parmhet or parmiv
gives P-values equivalent to those of the equal-variance F-test.

ivweight(newvarname) specifies the name of an output variable,
to be generated in the existing input dataset,
containing inverse-variance weights for the corresponding parameter estimates.
These inverse-variance weights can then be input as aweights
to the metaparm, module of the parmest package,
using the estimates, standard errors and degrees of freedom input to parmhet or parmiv,
to output estimates, confidence intervals and P-values
for summary parameters generated by a fixed-effect meta-analysis.

sweight(newvarname) specifies the name of an output variable,
to be generated in the existing input dataset,
containing semi-weights for the corresponding parameter estimates,
as described in Cochrane (1954).
These semi-weights can then be input as aweights
to the metaparm module of the parmest package,
using the estimates and degrees of freedom input to parmhet or parmiv
together with the standard errors generated using the sstderr() option,
to output estimates, confidence intervals and P-values
for summary parameters generated by a DerSimonian-Laird randomly-variable-effect meta-analysis,
as defined by DerSimonian and Laird (1986).

sstderr(newvarname) specifies the name of an output variable,
to be generated in the existing input dataset,
containing semi-weight-based standard errors for the corresponding parameter estimates.
These standard errors are equal to the inverse square roots
of the semi-weights generated by the sweight() option.
If calculated,
they can be input as the stderr() option
to the metaparm module of the parmest package,
using the estimates and degrees of freedom input to parmhet or parmiv,
together with aweights generated by the sweight() option,
to output estimates, confidence intervals and P-values
for summary parameters generated by a DerSimonian-Laird randomly-variable-effect meta-analysis,
as defined by DerSimonian and Laird (1986).

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.

DerSimonian, R. and Laird, N. 1986. Meta-analysis in clinical trials.
Controlled Clinical Trials 7(3): 177-188.

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_resultsset_opts,
parmhet_hettest_opts,
parmhet_resultsset

help for test

help for parmest, parmby, parmcip, metaparm, metan if installed