Stouffer’s meta-analysis with weight and direction effect in R

May 15, 2018
By

[This article was first published on R – Fabio Marroni's Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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))
}

To leave a comment for the author, please follow the link and comment on their blog: R – Fabio Marroni's Blog.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)