[R-meta] Sample sized weighted studies for metamean function

[Massimo Baudo]

Dear Community,

I have the following question regarding the metamean function in meta, and couldn’t find any previous threads on this regard.

I would like to calculate the pooled estimated mean of a follow-up time parameter: time from the first surgery to the second surgery. Studies report either mean (SD), or median (q1-q3). From my understanding (let me know if I am wrong), only the inverse of the variance is used to estimate the studies in metamean. This would be incorrect from my point of view to be used in this specific case, as the assumption is that larger studies have lower variance, thus weighted higher. However, for “follow-up” parameters larger studies could still have a large variance as some patients may have the whole follow-up length, while others would have a very short one. In this case, these larger studies would be weighted less (as if they were smaller studies), while smaller studies, which may have a smaller variance, would be weighted very high. Therefore, I would prefer to weight this parameter by the sample size. I could do it with metafor as follows:

MVR_data$weights = MVR_data$Samplesize / sum(MVR_data$Samplesize)
meta_analysis <- rma(yi = MVR_data$time_mean, vi = MVR_data$time_SD, weights = MVR_data$weights, data = MVR_data, method = “DL")
summary(meta_analysis)

This way though, I “loose” the very useful function of mean/SD approximation of the metamean function when dealing with median/q1/q3 or min/max.
Is there a possible solution to my problem with metamean?

Thank you for your support,

Massimo


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