[R-meta] Inner|outer model vs multiple random id terms in rma.mv

[Viechtbauer, Wolfgang (SP)]

Hi Divya,

These two formulations will only yield the same results when rho is estimated to be >= 0 (which is not the case in the second example).

Best,
Wolfgang

>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org]
>On Behalf Of Divya Ravichandar
>Sent: Wednesday, 29 April, 2020 19:00
>To: r-sig-meta-analysis using r-project.org
>Subject: Re: [R-meta] Inner|outer model vs multiple random id terms in
>rma.mv
>
>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