help metaparm_content_opts(Roger Newson)

Output-content options for metaparm

Syntax

by(varlist) Variables specifying by-groups

sumvar(varlist) Variables to be summed in output dataset

dfcombine(combination_rule) Rule for combining degrees of freedom

idnum(#) Value of numeric dataset ID variable

nidnum(newvarname) Name of numeric dataset ID variable

idstr(string) Value of string dataset ID variable

nidstr(newvarname) Name of string dataset ID variable

format(formatting_list) Display formats for variables in the output dataset

where combination_rule is

satterthwaite | welch | constant

and formatting_list is a list of form

varlist_1 format_1 ... varlist_n format_n

Description

These options are available for metaparm but not for parmcip.
They control the contents of the output dataset (or resultsset) created by metaparm.

Options

by(varlist) specifies a list of existing by-variables in the input dataset.
metaparm creates an output dataset with one observation in each by-group,
or with one observation only if by() is not specified,
and data on estimates, P-values, z- or t-statistics and confidence limits
for the weighted sums of parameters within the by-group,
or in the whole input dataset if by() is not specified.
The weightings for the weighted sums are specified using the weight specification.

sumvar(varlist) specifies a list of variables in the input dataset
to be included in the output dataset, with values equal to their unweighted sums in the input dataset
(if by() is not specified) or to their unweighted sums within the by-group
(if by() is specified). For instance, if the input dataset contains one observation
per study to be entered into a meta-analysis, and contains a variable N specifying the number
of subjects in the study, then the user can specify sumvar(N), and N will be present
in the output dataset, where it will contain the total number of subjects in all the studies.

dfcombine(combination_rule) specifies a rule for combining the degrees of freedom of the input parameters
to define the degrees of freedom for the output parameters,
if the t-distribution is used to define confidence limits and P-values.
If dfcombine(satterthwaite) is specified, then the formula of Satterthwaite (1946) is used.
If dfcombine(welch) is specified, then the formula of Welch (1947) is used.
If dfcombine(constant) is specified, then metaparm checks that the degrees of freedom
are constant (or constant within by-groups if by(varlist) is specified),
and then sets the output degrees of freedom to the constant input degrees of freedom.
dfcombine() is set to satterthwaite by default,
but is ignored if the t-distribution is not used to define confidence limits and P-values.
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 metaparm to estimate contrasts of interest, such as differences or interactions.
In these circumstances, using regress without the robust option
and using dfcombine(constant) with metaparm
gives confidence limits and P-values equivalent to those of the equal-variance t-test.
By contrast, if the user estimates group means using a separate constant-only model for each group,
and then uses metaparm with the option dfcombine(satterthwaite) or dfcombine(welch),
then the confidence limits and P-values
are equivalent to those of the Satterthwaite or Welch unequal-variance t-test.

idnum(#) specifies an ID number for the output dataset.
It is used to create a numeric variable, with default name idnum, in the output dataset,
with that value for all observations.
This is useful if the output resultsset is concatenated with other resultssets
using append.

nidnum(newvarname) specifies a name for the numeric ID variable
evaluated by idnum().
If idnum() is present and nidnum() is absent,
then the name of the numeric ID variable is set to idnum.

idstr(string) specifies an ID string for the output dataset.
It is used to create a string variable, with default name idstr, in the output dataset,
with that value for all observations.
This is useful if the output resultsset is concatenated with other resultssets
using append.

nidstr(newvarname) specifies a name for the string ID variable
evaluated by idstr().
If idstr() is present and nidstr() is absent,
then the name of the string ID variable is set to idstr.

format(varlist_1 format_1 ... varlist_n format_n)
specifies a list of pairs of variable lists and display formats.
The formats will be allocated to
the variables in the output dataset specified by the corresponding varlists.

Author

Roger Newson, King's College London, UK.
Email: roger.newson@kcl.ac.uk

References

Satterthwaite, F. E. 1946. An approximate distribution of estimates of variance components.
Biometrics Bulletin 2(6): 110-114.

Welch, B. L. 1947. The generalization of `Student's' problem when several different population variances are involved.
Biometrika 34(1/2): 28-35.

Also see

Manual: [D] append, [D] format

Help: [D] append, [D] format

parmest, parmby, parmcip, metaparm,
metaparm_outdest_opts, parmcip_opts,
metaparm_resultssets