With thanks as ever to Kit Baum (but particularly in this case, as I found a last-minute bug and had to ask him to re-upload...), I am very happy to introduce v3.0 of the admetan / ipdmetan meta-analysis command suite. The primary aim of this release was to separate out the functionalities of admetan and ipdmetan as far as possible. To that end, admetan can now be installed from SSC under its own name: that is, ssc install admetan works in the same way as ssc install ipdmetan; the package files are the same either way. Going forward, admetan can now directly be disseminated as an update to metan without any (potentially confusing) reference to individual participant data (IPD).
For those of you who attended the London Stata Conference back in September: sorry for the wait
Slides from that conference, including mine, can be found here: https://www.stata.com/meeting/uk18/
What’s new in admetan:
- a couple of new random-effects models, and a new syntax model() replacing re() [although the earlier syntax still works]
- a couple of continuity-correction alternatives as proposed by Sweeting et al (2004)
- the heterogeneity p-value can optionally be put back into the forest plot [I had quite a few aggrieved emails about that :-) ]
- other information such as pooled effect p-values can also now be added to the forest plot. This isn’t done via an option, but instead admetan’s saved “results sets” (and forestplot itself) now leave behind a variable named _EFFECT which contains the string concatenation of effect size and confidence limits which appears in the forest plot. This can be edited to include p-values etc. before running forestplot. More info is in the help file (see e.g. the penultimate example for forestplot)
- effect size, standard error, confidence limits, p-value, heterogeneity statistics etc. are now returned in matrices r(ovstat) [for overall] and r(bystats) [for subgroups]. These matrices are similar in structure to r(table) as returned by regression commands, and provide easy access to a much greater range of statistics
- with cumulative and influence meta-analyses, the saving(filename) option now provides access to heterogeneity statistics for each iteration, as well as effect sizes, standard errors etc. Indirectly, this allows such information to be displayed in a forest plot
- various minor bug fixes and improvements
What’s new in forestplot:
- a new option useopts which recalls forestplot options previously supplied to admetan, ipdmetan or ipdover and includes them in the current forestplot command line. The idea is that you can specify such options all in one go and not have to repeat yourself. In particular, you can use options such as counts, save and edit the “forestplot results set”, and run forestplot without needing to know how the “counts” information is stored
- diamonds are now drawn as polygons instead of line segments, and hence can be filled!
- various minor bug fixes and improvements
What’s new in ipdover:
- fixed the bug which meant no options could be specified to the command to the right of the colon
What’s new in ipdmetan:
- Nothing major (just the usual bug fixes and improvements) … but just to note that additional aggregate data [option ad(…) ] is now handled within ipdmetan.ado so that admetan.ado can stand alone.
I hope these routines prove useful to the Stata community! It'd be great if you could help spread the word to anyone who uses Stata for meta-analysis or for creating (e.g. trial subgroup) forest plots.
Many thanks,
David.
David Fisher
Statistician
MRC Clinical Trials Unit at UCL
e-mail: [email protected]
SSC TITLE
'ADMETAN': module to provide comprehensive meta-analysis
DESCRIPTION/AUTHOR(S)
The main routine, admetan, is intended as an update of the
popular Stata meta-analysis command ‘metan’, with greatly
extended functionality including a wide range of random-effects
models. The routine ‘forestplot’ is a stand-alone, re-written
and extended version of the graphics routine within ‘metan’.
‘admetan’ can save data in a format which ‘forestplot’
understands; together they allow extremely flexible and
generalised forest plots to be produced. Also included is an
“immediate” command ‘admetani’, which accepts numlists or
matrices as input rather than variables in memory. Finally, there
is ‘ipdmetan’, for two-stage individual participant data
(IPD) meta-analysis; and an associated command ‘ipdover’ for
creating forest plots of trial subgroups. For more information
on these commands, type ssc describe ipdmetan.
For those of you who attended the London Stata Conference back in September: sorry for the wait
Slides from that conference, including mine, can be found here: https://www.stata.com/meeting/uk18/
What’s new in admetan:
- a couple of new random-effects models, and a new syntax model() replacing re() [although the earlier syntax still works]
- a couple of continuity-correction alternatives as proposed by Sweeting et al (2004)
- the heterogeneity p-value can optionally be put back into the forest plot [I had quite a few aggrieved emails about that :-) ]
- other information such as pooled effect p-values can also now be added to the forest plot. This isn’t done via an option, but instead admetan’s saved “results sets” (and forestplot itself) now leave behind a variable named _EFFECT which contains the string concatenation of effect size and confidence limits which appears in the forest plot. This can be edited to include p-values etc. before running forestplot. More info is in the help file (see e.g. the penultimate example for forestplot)
- effect size, standard error, confidence limits, p-value, heterogeneity statistics etc. are now returned in matrices r(ovstat) [for overall] and r(bystats) [for subgroups]. These matrices are similar in structure to r(table) as returned by regression commands, and provide easy access to a much greater range of statistics
- with cumulative and influence meta-analyses, the saving(filename) option now provides access to heterogeneity statistics for each iteration, as well as effect sizes, standard errors etc. Indirectly, this allows such information to be displayed in a forest plot
- various minor bug fixes and improvements
What’s new in forestplot:
- a new option useopts which recalls forestplot options previously supplied to admetan, ipdmetan or ipdover and includes them in the current forestplot command line. The idea is that you can specify such options all in one go and not have to repeat yourself. In particular, you can use options such as counts, save and edit the “forestplot results set”, and run forestplot without needing to know how the “counts” information is stored
- diamonds are now drawn as polygons instead of line segments, and hence can be filled!
- various minor bug fixes and improvements
What’s new in ipdover:
- fixed the bug which meant no options could be specified to the command to the right of the colon
What’s new in ipdmetan:
- Nothing major (just the usual bug fixes and improvements) … but just to note that additional aggregate data [option ad(…) ] is now handled within ipdmetan.ado so that admetan.ado can stand alone.
I hope these routines prove useful to the Stata community! It'd be great if you could help spread the word to anyone who uses Stata for meta-analysis or for creating (e.g. trial subgroup) forest plots.
Many thanks,
David.
David Fisher
Statistician
MRC Clinical Trials Unit at UCL
e-mail: [email protected]
SSC TITLE
'ADMETAN': module to provide comprehensive meta-analysis
DESCRIPTION/AUTHOR(S)
The main routine, admetan, is intended as an update of the
popular Stata meta-analysis command ‘metan’, with greatly
extended functionality including a wide range of random-effects
models. The routine ‘forestplot’ is a stand-alone, re-written
and extended version of the graphics routine within ‘metan’.
‘admetan’ can save data in a format which ‘forestplot’
understands; together they allow extremely flexible and
generalised forest plots to be produced. Also included is an
“immediate” command ‘admetani’, which accepts numlists or
matrices as input rather than variables in memory. Finally, there
is ‘ipdmetan’, for two-stage individual participant data
(IPD) meta-analysis; and an associated command ‘ipdover’ for
creating forest plots of trial subgroups. For more information
on these commands, type ssc describe ipdmetan.
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