I'm trying to do meta-regression with a lot of trials (>40 trials with >100 'arms') investigating the efficacy of a procedure (abl) and any 'addon' procedure. Each trial will have 2 or more arms. In some trials, the 'control' arm is no procedure; in others is a 'vanilla' procedure, with the active arm adding in various 'extras'.

I want to see not only if the procedure improves outcomes, but also if various 'extras' to the procedure also influence outcome.

My columns which are predictors in my model start with 'pred_'. If they are to do with the procedure used (abl), they start 'pred_abl_'

> head(af_dat)
        id arm  xi  ni pred_age pred_gender pred_antiarrhythmics pred_abl_pvi_done pred_abl_pvi_type pred_abl_cryo pred_abl_cfea pred_abl_gp pred_abl_lines pred_abl_mapping                      pred_endpoint pred_at_included pred_blanking        pi
1 23094720   A 112 146     56.0        68.0                    0                 1                 1             0             0           0              1                1                             Holter                0             3 0.7671233
2 23094720   B 103 148     54.0        72.0                    1                 0                 0             0             0           0              0                0                             Holter                0             3 0.6959459
3 21539635   A  45  58     57.6        66.0                    0                 1                 1             0             1           0              0                1                            Holter                 1             3 0.7758621
4 21539635   B  14  24     56.4        67.0                    0                 0                 0             0             1           0              0                1                             Holter                1             3 0.5833333
5 21539635   C  27  35     52.2        71.0                    0                 1                 1             0             0           0              0                1                             Holter                1             3 0.7714286
6 24549549   A  50  66     56.3        77.3                    0                 1                 1             0             0           0              0                0 Mobile telemetry (transtelephonic)                1             3 0.7575758

The column which indicates if they had a procedure at all is pred_abl_pvi_done. The other columns beginning pred_abl_* (of which there are 6 shown above) describe the presence of any 'extras' to the procedure, and must be 0 if the pred_abl_pvi_done is 0, by definition.

Initially, I was going to do the meta-regression as follows:

rma(measure='PLO', xi=xi, ni=ni, data=af_dat, mods=~pred_age+pred_gender+pred_antiarrhythmics+pred_abl_pvi_done+pred_abl_pvi_type+pred_abl_cryo+pred_abl_cfea+pred_abl_gp+pred_abl_lines+pred_abl_mapping+pred_endpoint+pred_at_included+pred_blanking)

However, I'm not sure if this is valid; the pred_abl_* columns will of course be very strongly correlated with pred_abl_pvi_done as they will be 0 if the latter is 0. If the 'abl' procedure (pred_abl_pvi_done=1) is very efficacious, I'm worried the other pred_abl_* columns will also appear correlated even if they add nothing prognostically, as many of the '0's for these columns will merely be indicating no procedure has been done at all.

One way to get around this, I suppose, would be to only look at the 6 procedural columns if pred_abl_pvi_done is 1.

I could therefore do 2 analysies:

1. Multivariate regression of all the studies, excluding the 6 procedural columns. I would then see if a procedure versus no procedure (pred_abl_pvi_done 1 or 0) is significant
2. Select only cases where pvi_abl_pvi_done = 1 (ie the procedure is done (90% of the trials), and exclude medical treatment), and THEN do a further multivariate regression where I include all the procedural questions.

Do you think I need to do these 2 analyses separately due to the correlation?