How to best handle subscores in a meta-analysis? I am conducting a meta-analysis of effect sizes d in R using the metafor package. d represents differences in memory scores between patients and healthy. However some studies report only subscores of the measure of interest d (e.g. several different memory scores or scores from three separate blocks of memory testing). Please see the following dummy data set with d representing the studies' effect sizes as well as their standard deviations sd.: 
d <- round(rnorm(5,5,1),2)
sd <- round(rnorm(5,1,0.1),2)
study <- c(1,2,3,3,3)
subscore <- c(1,1,1,2,3)
my_data <- as.data.frame(cbind(study, subscore, d, sd))

library(metafor)
m1 <- rma(d,sd, data=my_data)
summary(m1)

I would like to ask for your opinion for the best way how to handle these subscores - e.g.:


*

*Select one subscore from each study that reports more than one
    score.

*Include all subscores (this would violate the assumption
    of independency of the rfx model as subscores of one study come from
    the same sample) 

*For each study that reports subscores: calculate
    an average score & average standard deviation and include this
    "merged effect size" in the rfx meta-analysis.  

*Include all
    subscores and add a dummy variable indicating from which study a
    certain score is derived.

 A: I agree it's a tricky situation. These are just a few thoughts.
Whether to average d effect sizes:
If you're not interested in subscales, then my first choice would be to take the average effect size for the subscales in a given study. 
That assumes that all subscales are equally relevant to your research question. If some scales are more relevant, then I might just use those subscales.
If you are interested in differences between subscales, then it makes sense to include the effect size for each subscale coded for type.
Standard error of d effect sizes: Presumably you are using a formula to calculate the standard error of d based on the value of d and the group sample sizes. Adapting this formula, we get
$$se(d) = \sqrt{\left( \frac{n_1 + n_2}{n_1 n_2} + \frac{d^2}{2(n_1+n_2-2)}\right) \left(\frac{n_1 + n_2}{n_1+n_2-2} \right)}, $$

where $n_1$ and $n_2$ are the sample sizes of the two groups being
  compared and $d$ is Cohen's $d$.

I imagine you could apply such a formula to calculate the standard error to the average d value for the subscales.
