[R-meta] Meta - Bug with REML or small N?

Tue, Mar 21, 2023 10:13 AM

So the REML estimate is correct:

dat <- structure(list(study = c("AA", "BB", "CC",
"EE", "DDD"), en = c(9, 41, 29, 8, 13
), em = c(32, 27.5, 28.7, 22.8, 30.5), esd = c(1.9, 3.8, 5.2,
3.8, 4.9), cn = c(8, 26, 28, 10, 14), cm = c(30.1, 24.9, 26.9,
24.7, 30.2), csd = c(2.4, 3.6, 3, 4.3, 5.1)), row.names = c(NA,
-5L), class = c("tbl_df", "tbl", "data.frame"))

dat <- escalc(measure="MD", m1i=em, sd1i=esd, n1i=en, m2i=cm, sd2i=csd, n2i=cn, data=dat)

res <- rma(dat)
res
profile(res, steps=100)

Rather different tau^2 estimates depending on the estimator:

methods <- c("ML","REML","EB","PM","HS","HSk","HE","DL","SJ")
data.frame(tau2=sapply(methods, \(x) round(rma(dat, method=x)$tau2, 4)))

Sometimes life is difficult.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Jorge Teixeira via R-sig-meta-analysis
Sent: Tuesday, 21 March, 2023 17:56
To: Michael Dewey
Cc: Jorge Teixeira; R Special Interest Group for Meta-Analysis
Subject: Re: [R-meta] Meta - Bug with REML or small N?

Thanks Michael and Wolfgang.

Here goes the df:

structure(list(study = c("AA", "BB", "CC",
"EE", "DDD"), en = c(9, 41, 29, 8, 13
), em = c(32, 27.5, 28.7, 22.8, 30.5), esd = c(1.9, 3.8, 5.2,
3.8, 4.9), cn = c(8, 26, 28, 10, 14), cm = c(30.1, 24.9, 26.9,
24.7, 30.2), csd = c(2.4, 3.6, 3, 4.3, 5.1)), row.names = c(NA,
-5L), class = c("tbl_df", "tbl", "data.frame"))

Thanks

Michael Dewey <lists at dewey.myzen.co.uk> escreveu no dia ter?a, 21/03/2023
?(s) 16:36:

Dear Jorge

It looks as though the issue is that REML estimates tau^2 to be
effectively zero whereas DL has a positive estimate. The different ways
of estimating tau^2 do often differ, sometimes by quite surprising
amounts. Whether that is related to the smallish number of studies I
could not say but, at the moment, there does not seem to be evicence of
anything wrong with the implementation of REML.

Michael

On 21/03/2023 14:57, Jorge Teixeira via R-sig-meta-analysis wrote:

Hi.

Let me know if you need me to provide the data for this example.
Screenshots in the bottom.

I ran this MA with REML, and the weight for random and common effects
are exactly the same! Never saw anything like this. t2values also don?t
look plausible.

*_#1_*
vo2 <- metacont(en  , em, esd, cn, cm, csd, study, method.tau = "REML",
prediction = TRUE, data = dat_vo2, sm = "MD")
vo2

Is this a bug or a particular issue of low number of studies and low
sample size?

*_#2_*
vo2 <- metacont(en  , em, esd, cn, cm, csd, study, method.tau = "DL",
prediction = TRUE, data = dat_vo2, sm = "MD")
vo2

I ran this with DL estimator and weights and t2are plausible. I also ran
other similar MA using REML and this was all okay.

#1 using SMD instead of MD also looks fine.


Thanks,
Jorge


*_REML:_*

image.png


*_DL:_*
image.png

[R-meta] Meta - Bug with REML or small N?

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