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

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

- 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

- 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(

- various minor bug fixes and improvements

What’s new in

- a new option useopts which recalls

- 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

- fixed the bug which meant no options could be specified to the command to the right of the colon

What’s new in

- Nothing major (just the usual bug fixes and improvements) … but just to note that additional aggregate data [option ad(…) ] is now handled within

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: d.fisher@ucl.ac.uk

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.

**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: d.fisher@ucl.ac.uk

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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