[R-meta] Inner|outer model vs multiple random id terms in rma.mv
Divya Ravichandar
d|vy@ @end|ng |rom @econdgenome@com
Wed Apr 29 19:00:07 CEST 2020
Hi all
Following a recommendation from Prof.Wolfgang to make access to input data
easier, I have reformatted the above example to avoid using an external csv
file and such.
Hi all,
I am trying to understand why results from running a model of the form
~lvl1|lv2 are not comparable to results of running ~1 | lvl1 ,~ 1 | lvl2
In a simple example (case_simple in code below),results of the 2 models are
comparable as expected.
However, when running the 2 models on a more complex example (case_complex)
markedly different results are obtained with ~ Dataset | Cohort estimating
a pvalue of .02 and list(~ 1 | Dataset,~ 1 | Cohort) estimating a pvalue of
.2
Thank you
*Reproducible example*
library(metafor)
# example where results of the 2 models agree
case_simple <- data.frame(Dataset=
c("a","b","c","d"),Cohort=c("c1","c1","c2","c3"), Tech=
c("a1","a2","a1","a1"),Effect_size=c(-1.5,-3,1.5,3),Standard_error=c(.2,.4,.2,.4))
res1 = rma.mv(Effect_size, Standard_error^2, random = list(~ 1 | Dataset,~
1 | Cohort), data=case_simple)
res2=rma.mv(Effect_size, Standard_error^2, random = ~ Dataset | Cohort,
data=case_simple)
# example where results of the 2 models DONT agree
case_complex <-
data.frame(Dataset=c("Dt1","Dt2","Dt3","Dt4","Dt5","Dt5","Dt6","Dt7","Dt8","Dt9"),Cohort=c("C1","C2",rep("C3",5),rep("C4",2),"C5"),
Effect_size=c(-0.002024454,-0.003915314,-0.042282757,-1.43826175,-0.045423574,-0.17682309,-21.72691245,-2.559727204,-0.091972279,-0.763332081),
Standard_error=c(0.15283972,0.117452325,0.262002289,0.555230971,0.708917912,0.682989908,2.704749864,1.40514335,0.735696048,0.713557015))
res1 = rma.mv(Effect_size, Standard_error^2, random = list(~ 1 | Dataset,~
1 | Cohort), data=case_complex)
res2=rma.mv(Effect_size, Standard_error^2, random = ~ Dataset | Cohort,
data=case_complex)
On Wed, Apr 22, 2020 at 9:51 AM Divya Ravichandar <divya using secondgenome.com>
wrote:
> Hi all,
>
> I am trying to understand why results from running a model of the form
> ~lvl1|lv2 are not comparable to results of running ~1 | lvl1 ,~ 1 | lvl2
>
> In a simple example case below,results of the 2 models are comparable as
> expected.
>
> ```case <- data.frame(Dataset=
> c("a","b","c","d"),Cohort=c("c1","c1","c2","c3"), Tech=
> c("a1","a2","a1","a1"),Effect_size=c(-1.5,-3,1.5,3),Standard_error=c(.2,.4,.2,.4))
> res1 = rma.mv(Effect_size, Standard_error^2, random = list(~ 1 |
> Dataset,~ 1 | Cohort), data=case)
> res2=rma.mv(Effect_size, Standard_error^2, random = ~ Dataset | Cohort,
> data=case)
> ```
> However, when running the 2 model on a more complex example [attached]
> markedly different results are obtained with ~ Dataset | Cohort
> estimating a pvalue of .02 and list(~ 1 | Dataset,~ 1 | Cohort)
> estimating a pvalue of .2
> --
> *Divya Ravichandar*
> Scientist
> Second Genome
>
--
*Divya Ravichandar*
Scientist
Second Genome
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