# [R-meta] How does rma handle effect size of zero

Divya Ravichandar d|vy@ @end|ng |rom @econdgenome@com
Mon Jun 8 22:01:11 CEST 2020

```Hi all

Apologies for a second email but I wanted to clarify the code provided in
the example above. Corrected code below

library(metafor)
library(magrittr)
case1 <- data.frame(Study= c("a","b","c","d"),ES=rep(.1,4),SE=rep(1e-5,4))
%>% rma(ES, SE^2, data=.) #non zero case
case2 <- data.frame(Study= c("a","b","c","d"),ES=rep(0,4),SE=rep(1e-5,4))
%>% rma(ES, SE^2, data=.) #zero case

On Mon, Jun 8, 2020 at 12:59 PM Divya Ravichandar <divya using secondgenome.com>
wrote:

> Hi all
>
> I would like to get some understanding around how rma handles effect sizes
> of 0. A test example is outlined below
>
> library(metafor)
> library(magrittr)
>
> case1 <- data.frame(Study= c("a","b","c","d"),ES=rep(.1,4),SE=rep(1e-5,4))
> %>% rma(ES, SE^2, data=.) #non zero case
> meta_case1 <- rma(ES, SE^2,  data=case1)
> case2 <- data.frame(Study= c("a","b","c","d"),ES=rep(0,4),SE=rep(1e-5,4))
> %>% rma(ES, SE^2, data=.) #zero case
>
> While case 1 results in a significant p value  (<.0001), case 2 results in
> a non-significant p value (1). Does a zero effect size violate any
> assumptions and if not I wonder why a consistent estimate of 0 across
> datasets results in a non-significant result?
>
> Thanks
>
> --
> *Divya Ravichandar*
> Scientist
> Second Genome
>

--
*Divya Ravichandar*
Scientist
Second Genome

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