[R-meta] Bivariate generalized linear mixed model with {metafor}

Wed, Mar 9, 2022 2:46 PM

Different parameterizations of the same model.

Also, the correlations seems like they just flipped signs, but they are really different things and I suspect it's just coincidence that they happen to be so close in absolute value. 

With (group | study), you have a random intercept (for the control group logit risk) and a random slope for the group/treatment effect (for the log odds ratio).

With (control+treat-1|study), you have random effects for the control and treatment group logit risks. This is the same as (0 + group | study).

So really different things are being correlated here. But in the end, it's the same model, parameterized in different ways.

Best,
Wolfgang

-----Original Message-----
From: Arthur Albuquerque [mailto:arthurcsirio at gmail.com]
Sent: Wednesday, 09 March, 2022 23:35
To: r-sig-meta-analysis at r-project.org; Viechtbauer, Wolfgang (SP)
Subject: RE: [R-meta] Bivariate generalized linear mixed model with {metafor}

Very enlightening code, thank you!!

I noted that you used a different lme4 syntax for this model in your article with
Jackson et al with?a ?control? indicator and removed the intercept within the
random effect.

I compared both syntax outputs. Results are all the same, except the random
effect correlation parameter (opposite results). Do you know why?

library(metafor)
library(lme4)

dat <- structure(list(study.name = c("Cutler1995", "Dahlof1991", "Dowson2002",
"Ensink1991", "Geraud2000", "Goadsby1991", "Goadsby2000", "Havanka2000",
"Kaniecki2006", "Mathew2003", "Myllyla1998", "Nappi1994", "Patten1991",
"Pfaffenrath1998", "Sandrini2002", "Sargent1995", "Sheftell2005", "Tfelt-
Hansen1995", "Tfelt-Hansen1998", "Visser1996"), n1 = c(66L, 275L, 194L, 131L,
504L, 89L, 129L, 98L, 131L, 831L, 42L, 142L, 142L, 277L, 170L, 46L, 902L, 122L,
388L, 72L), n2 = c(65L, 182L, 99L, 78L, 56L, 93L, 142L, 91L, 127L, 419L, 41L,
81L, 101L, 91L, 84L, 47L, 892L, 126L, 160L, 85L), r1 = c(37L, 180L, 123L, 60L,
304L, 45L, 63L, 59L, 64L, 471L, 33L, 73L, 95L, 175L, 85L, 26L, 649L, 63L, 239L,
33L), r2 = c(17L, 48L, 42L, 14L, 24L, 9L, 30L, 28L, 47L, 105L, 12L, 25L, 22L,
27L, 25L, 8L, 375L, 30L, 64L, 15L)), row.names = c(NA, 20L), class =
"data.frame")

dat <- to.long(measure="OR", ai=r1, n1i=n1, ci=r2, n2i=n2, data=dat)

res <- glmer(cbind(out1,out2) ~ group + (group | study), data=dat,
family=binomial)
summary(res)

VarCorr(res)
# Corr = -0.68

dat_mod = dat
dat_mod$treat = ifelse(dat$group==1, 1, 0)
dat_mod$control = ifelse(dat$group==2, 1, 0)

model6 = glmer(cbind(out1,out2)~factor(treat)+(control+treat-1|study),
?data=dat_mod, family=binomial(link="logit"))

summary(model6)

VarCorr(model6)
# Corr = 0.686

Best,

Arthur M. Albuquerque

Medical student
Universidade Federal do Rio de Janeiro, Brazil

On Mar 9, 2022, 6:54 PM -0300, Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl>, wrote:

Ok, so here is what I came up with:

library(metafor)
library(lme4)

dat <- structure(list(study.name = c("Cutler1995", "Dahlof1991", "Dowson2002",
"Ensink1991", "Geraud2000", "Goadsby1991", "Goadsby2000", "Havanka2000",
"Kaniecki2006", "Mathew2003", "Myllyla1998", "Nappi1994", "Patten1991",
"Pfaffenrath1998", "Sandrini2002", "Sargent1995", "Sheftell2005", "Tfelt-
Hansen1995", "Tfelt-Hansen1998", "Visser1996"), n1 = c(66L, 275L, 194L, 131L,
504L, 89L, 129L, 98L, 131L, 831L, 42L, 142L, 142L, 277L, 170L, 46L, 902L, 122L,
388L, 72L), n2 = c(65L, 182L, 99L, 78L, 56L, 93L, 142L, 91L, 127L, 419L, 41L,
81L, 101L, 91L, 84L, 47L, 892L, 126L, 160L, 85L), r1 = c(37L, 180L, 123L, 60L,
304L, 45L, 63L, 59L, 64L, 471L, 33L, 73L, 95L, 175L, 85L, 26L, 649L, 63L, 239L,
33L), r2 = c(17L, 48L, 42L, 14L, 24L, 9L, 30L, 28L, 47L, 105L, 12L, 25L, 22L,
27L, 25L, 8L, 375L, 30L, 64L, 15L)), row.names = c(NA, 20L), class =
"data.frame")

# double-check that we can reproduce OR = 3.50 (95% CI, 2.94 to 4.16) for
# the two-stage random-effects model

res <- rma(measure="OR", ai=r1, n1i=n1, ci=r2, n2i=n2, data=dat, method="DL")
predict(res, transf=exp, digits=2)

# the model being fitted is essentially this one

res <- rma.glmm(measure="OR", ai=r1, n1i=n1, ci=r2, n2i=n2, data=dat,
model="UM.RS", cor=TRUE)
summary(res)
predict(res, transf=exp, digits=2)

# we can also fit this model directly with glmer()

dat <- to.long(measure="OR", ai=r1, n1i=n1, ci=r2, n2i=n2, data=dat)

res <- glmer(cbind(out1,out2) ~ group + (group | study), data=dat,
family=binomial)
summary(res)
round(exp(c(fixef(res)[2], confint(res, parm="group1", method="Wald"))), 2)

# note the exact same pooled OR and 95% CI

# however, the authors report the marginal OR, so for this we fit the model as

res <- glmer(cbind(out1,out2) ~ 0 + group + (0 + group | study), data=dat,
family=binomial)
summary(res)

# and then we can estimate the marginal OR as described in B.2

cval <- 16*sqrt(3) / (15*pi)
p1hat <- 1 / (1 + exp(-fixef(res)[1] / sqrt(1 + cval^2 *
VarCorr(res)[[1]][1,1])))
p2hat <- 1 / (1 + exp(-fixef(res)[2] / sqrt(1 + cval^2 *
VarCorr(res)[[1]][2,2])))
round((p2hat / (1 - p2hat)) / (p1hat / (1 - p1hat)), 2)

# there is the 3.48 that the authors report; getting the CI for this marginal OR
# requires a bit more work using the delta method

So, to summarize: The model they describe *is* 'Model 6'. You can also fit this
with rma.glmm() as shown above (with the 'cor=TRUE' option, which I hadn't
implemented yet when we wrote that 2018 paper). I don't know why they focus on
the marginal OR, but I didn't read the article fully.

Best,
Wolfgang

-----Original Message-----
From: Arthur Albuquerque [mailto:arthurcsirio at gmail.com]
Sent: Wednesday, 09 March, 2022 20:37
To: r-sig-meta-analysis at r-project.org; Viechtbauer, Wolfgang (SP)
Subject: RE: [R-meta] Bivariate generalized linear mixed model with {metafor}

Dear Wolfgang,

Thank you in advance. I contacted Reference [1] authors, and they sent me this
data:

structure(list(study.name = c("Cutler1995", "Dahlof1991", "Dowson2002",
"Ensink1991", "Geraud2000", "Goadsby1991", "Goadsby2000", "Havanka2000",
"Kaniecki2006", "Mathew2003", "Myllyla1998", "Nappi1994", "Patten1991",
"Pfaffenrath1998", "Sandrini2002", "Sargent1995", "Sheftell2005", "Tfelt-
Hansen1995", "Tfelt-Hansen1998", "Visser1996"), n1 = c(66L, 275L, 194L, 131L,
504L, 89L, 129L, 98L, 131L, 831L, 42L, 142L, 142L, 277L, 170L, 46L, 902L, 122L,
388L, 72L), n2 = c(65L, 182L, 99L, 78L, 56L, 93L, 142L, 91L, 127L, 419L, 41L,
81L, 101L, 91L, 84L, 47L, 892L, 126L, 160L, 85L), r1 = c(37L, 180L, 123L, 60L,
304L, 45L, 63L, 59L, 64L, 471L, 33L, 73L, 95L, 175L, 85L, 26L, 649L, 63L, 239L,
33L), r2 = c(17L, 48L, 42L, 14L, 24L, 9L, 30L, 28L, 47L, 105L, 12L, 25L, 22L,
27L, 25L, 8L, 375L, 30L, 64L, 15L)), row.names = c(NA, 20L), class =
"data.frame")

Best,

Arthur M. Albuquerque

Medical student
Universidade Federal do Rio de Janeiro, Brazil

On Mar 8, 2022, 7:07 PM -0300, Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl>, wrote:

Hi Arthur,

I don't want to start re-reading all those articles right now, but who cites who
isn't the best indication of whether people are talking about the same model or
not.

In [1], the authors use data from a Cochrane review to illustrate the model. If
you provide these data here using dput(), so that I can immediately reproduce the
exact dataset, then I could see whether I can reproduce their results. It's time-
consuming extracting data manually from pdfs, so I'll leave this up to you
whether you want to do this.

Best,
Wolfgang

-----Original Message-----
From: Arthur Albuquerque [mailto:arthurcsirio at gmail.com]
Sent: Tuesday, 08 March, 2022 2:07
To: r-sig-meta-analysis at r-project.org; Viechtbauer, Wolfgang (SP)
Subject: RE: [R-meta] Bivariate generalized linear mixed model with {metafor}

Hi Wolfgang,

It?s me again about this bivariate model. I am having a hard time trying to
figure out if I understood it correctly.

To recap, I wanted to fit a bivariate meta-analysis model (hereafter, mod1)
described in Reference [1] below. You replied suggesting it was the "Model 6: the
Van Houwelingen bivariate? (mod2) in your article with Jackson et al (Reference
[2]).

However, I am now re-reading all these articles and I believe mod1 and mod2 are
different. Reference [1] cites Thompson et al. (Reference [3]), and does not cite
van Houwelingen. You cited Van Houwelingen et al (Reference [4]). To my
knowledge, they seem different models indeed.

In fact, Van Houwelingen in Reference [5] directly cites Thompson suggesting
these models are distinct:

"The mix of many ?fixed and a few random e?ffects as proposed by Thompson et al.
? are more in the spirit of the functional approach. These methods are meant to
impose no conditions on the distribution of the true baseline risks? In a letter
to the editor by Van Houwelingen and Senn following the article of Thompson et
al. , Van Houwelingen and Senn argue that putting Bayesian priors on all nuisance
parameters, as done by Thompson et al., does not help solving the inconsistency
problem."

Are they indeed different model?

Please ignore this email if this question is out of the scope of your mailing
list. Sorry in advance.

Kind regards,

Arthur M. Albuquerque

Medical student
Universidade Federal do Rio de Janeiro, Brazil

References:

[1] Xiao, Mengli, Yong Chen, Stephen R Cole, Richard F MacLehose, David B
Richardson, and Haitao Chu. ?Controversy and Debate: Questionable Utility of the
Relative Risk in Clinical Research: Paper 2: Is the Odds Ratio ?Portable? in
Meta-Analysis? Time to Consider Bivariate Generalized Linear Mixed Model?.
Journal of Clinical Epidemiology 142 (February 2022): 280?87.
https://doi.org/10.1016/j.jclinepi.2021.08.004

[2] Jackson, Dan, Martin Law, Theo Stijnen, Wolfgang Viechtbauer, and Ian R.
White. ?A Comparison of Seven Random-Effects Models for Meta-Analyses That
Estimate the Summary Odds Ratio?. Statistics in Medicine 37, no. 7 (30 March
2018): 1059?85. https://doi.org/10.1002/sim.7588

[3] Thompson, Simon G., Teresa C. Smith, and Stephen J. Sharp. ?Investigating
Underlying Risk as a Source of Heterogeneity in Meta-Analysis?. Statistics in
Medicine 16, no. 23 (15 December 1997): 2741?58.
https://doi.org/10.1002/(SICI)1097-0258(19971215)16:23<2741::AID-SIM703>3.0.CO;2-
0

[4] Van Houwelingen, Hans C., Koos H. Zwinderman, and Theo Stijnen. ?A Bivariate
Approach to Meta-Analysis?. Statistics in Medicine 12, no. 24 (30 December 1993):
2273?84. https://doi.org/10.1002/sim.4780122405

[5] Houwelingen, Hans C. van, Lidia R. Arends, and Theo Stijnen. ?Advanced
Methods in Meta-Analysis: Multivariate Approach and Meta-Regression?. Statistics
in Medicine 21, no. 4 (28 February 2002): 589?624.
https://doi.org/10.1002/sim.1040

[R-meta] Bivariate generalized linear mixed model with {metafor}

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