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I often have to perform meta-analysis of past experiments for which the only info I have is the fold change and the p-value (of any measure you may imagine: species richness, gene expression, depth of coverage, plates of carbonara eaten in 5 minutes, everything).
The hardest thing to find out for me was how to take the direction of the changes into account. E.g. if monday I eat 10 carbonara more than my brother (p=0.01), on tuesday 10 more (p=0.01), on wednesday 5 more (p=0.1), on thursday 10 less (p=0.01), on friday ten less (p=0.01) and on saturday 5 less (p=0.1), a standard Stouffer meta-analysis would return a highly significant p-value, completely disregarding the fact that the significant changes were half in my favor, and half in favor of my brother.
How can I take into account the information on the direction?
I had no idea on how to proceed, and I wasn’t able to find any reference (I am not an expert of meta-analysis), but then I was so lucky to stumble upon the manual page of the software package METAINTER for performing meta-analysis.

The authors described there a method to perform Stouffer meta-analysis accounting for the direction of the effects. Their explanation was so clear that it was easy for me to write a simple function in R: I paste it below.

```signed.Stouffer.meta <- function(p, w, sign) { # p is a vector of p-values
if (missing(w)) {
w <- rep(1, length(p))/length(p)
} else {
if (length(w) != length(p))
stop("Length of p and w must equal!")
}
if(length(p)==1) Zi1)
{
Zi<-qnorm(p/2,lower.tail=FALSE)
Zi[sign<0]<-(-Zi[sign<0])
}
Z  <- sum(w*Zi)/sqrt(sum(w^2))
p.val <- 2*(1-pnorm(abs(Z)))
return(c(Z = Z, p.value = p.val))
}
```