[R-meta] complex data structure for a meta-analysis

[Yogev Kivity]

Hi everyone,I am running a meta-analysis using 'metafor' and I came across several questions that I could not find answers
for in 'metafor's documentation.

In short, were are examining psychotherapy data, and how a specific measure collected at the beginning of treatment (attachment style in relationships) predicts outcome of therapy as measured at post-treatment. Both measures are usually dimensional, so we are using Pearson's r which we then convert to Fisher's z.

The design of the meta-analysis is multilevel and multivariate in that each study usually includes several different treatment groups with different patients, as well as several subscales of attachment (e.g., level of anxiety in attachment and level of avoidance in attachment) and several measures of outcome at post-treatment (e.g., anxiety, depression etc.). This is complicated by the fact that studies rarely use the same attachment and outcome measures, and for the most part, we do not have data on the covariance among these measures.

I am assuming that our design is most similar to Konstantopoulos (2011), but we have an additional level of effect sizes repeated within groups, so basically we have multiple effect size per treatment arm, nested within treatment arm, which in are turn nested within study. Would that be correct?

My main questions are:
1. what would be the best approach for modeling all of these levels of analyses, while taking into account the fact that the effect sizes within treatment arm are likely no independent. My understanding is that usually multivariate is interpreted to mean multiple outcome measures, but in our case we have multiple outcome as well as multiple predictors.

2. How should I squared be calculated for such models?

3. is there an extension of funnel plots to multi-level models that could reliably represent the data? I guess that using the standard funnel plot ignores the mutilevel structure of the data, is that correct?

Best,
Yogev



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