Hi,

I am running a metanalysis to look at pooled estimates of attrition using metaprop. I have generated a forest plot as attached. How do I test subgroup differences within this analysis? i.e is there a significant difference between men and women pooled attrition estimate?

Many thanks,
Carla



Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input str20 Study float Year int size byte attrition int completed float(sex totalbysex _ES _seES _LCI _UCI _WT)
"Aufses"     1998   88 11   52 0   63        .125 .035254754   .07125274  .21011756  1.951692
"Bergen"     1998  132 11   92 0  103   .08333334  .02405626   .04716797  .14306453  3.186563
"Brown"      2014   85  9   40 0   49   .10588235  .03337334   .05671241   .1891352 2.1106992
"Carter"     2018   88 11   52 0   63        .125 .035254754   .07125274  .21011756  1.951692
"Dodson"     2004  120 11   76 0   87   .09166667  .02634133   .05195718   .1567085 2.8715415
"Gifford"    2014  371 48  128 0  176   .12938005 .017424528    .0989906   .1673659 4.3181467
"Nadeem"     2014  106 29   55 0   84    .2735849  .04329977    .1977593  .36524725   1.42592
"Sullivan"   2013 2033 81 1236 0 1317    .0398426 .004337868  .032172184  .04924871  6.828648
"Symer"      2018  792 84  421 0  505    .1060606  .01094129   .08648507  .12943918  5.662387
"Yaghoubian" 2012  348 29  191 0  220   .08333334 .014815813   .05864695  .11711818  4.844076
"Yeo"        2017  836 90  438 0  528    .1076555 .010719666   .08841137  .13048883  5.708778
"Yeo"        2010 6303 50 2641 0 2691   .00793273 .001117399  .006022631  .01044226  7.082663
"Aufses"     1998   88  8   17 1   25    .0909091  .03064545   .04678642   .1692539 2.3720863
"Bergen"     1998  132  7   22 1   29    .0530303 .019504873   .02592242   .1054179  3.928538
"Brown"      2014   85  7   29 1   36   .08235294 .029817274   .04046373  .16035984 2.4594166
"Carter"     2018   88  8   17 1   25    .0909091  .03064545   .04678642   .1692539 2.3720863
"Dodson"     2004  120  9   24 1   33        .075  .02404423   .03995712  .13640918 3.1883204
"Gifford"    2014  371 39   73 1  112    .1051213  .01592357    .0778562     .14048  4.616435
"Nadeem"     2014  106 12   10 1   22   .11320755 .030774835   .06595682   .1875127 2.3587935
"Sullivan"   2013 2033 50  666 1  716  .024594195 .003435107  .018704975 .032276634  6.927888
"Symer"      2018  792 69  218 1  287    .0871212  .01002088  .069419935  .10880835  5.853562
"Yaghoubian" 2012  348 26  102 1  128   .07471264 .014094372   .05149312  .10721887   4.99494
"Yeo"        2017  836 74  234 1  308   .08851675   .0098239    .0710965  .10970127  5.893878
"Yeo"        2010 6303 27 1241 1 1268 .0042836745 .000822626 .0029457496 .006225475  7.091249
end
label values sex sex
label def sex 0 "Male", modify
label def sex 1 "Female", modify
------------------ copy up to and including the previous line ------------------

Listed 24 out of 24 observations