Lukasz Stasielowicz
Osnabr?ck University
Institute for Psychology
Research methods, psychological assessment, and evaluation
Seminarstra?e 20
49074 Osnabr?ck (Germany)
Am 18.01.2022 um 12:00 schrieb r-sig-meta-analysis-request at r-project.org:
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> Today's Topics:
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> 1. Re: Bivariate generalized linear mixed model with {metafor}
> (Arthur Albuquerque)
> 2. Question on effect sizes (David Pedrosa)
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 17 Jan 2022 23:53:15 -0300
> From: Arthur Albuquerque <arthurcsirio at gmail.com>
> To: "r-sig-meta-analysis at r-project.org"
> <r-sig-meta-analysis at r-project.org>, Michael Dewey
> <lists at dewey.myzen.co.uk>, "Viechtbauer, Wolfgang (SP)"
> <wolfgang.viechtbauer at maastrichtuniversity.nl>
> Subject: Re: [R-meta] Bivariate generalized linear mixed model with
> {metafor}
> Message-ID: <8132eb7d-78f5-48cf-a81c-535aba9618e1 at Spark>
> Content-Type: text/plain; charset="utf-8"
>
> Dear Wolfgang,
>
> We had this discussion back in October, so you might not remember. In brief, I wanted to fit a Bivariate model and you pointed towards the Model 6 in your excellent article:
>
> Jackson, D., Law, M., Stijnen, T., Viechtbauer, W., & White, I. R. (2018). A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio. Statistics in Medicine, 37(7), 1059-1085. https://doi.org/10.1002/sim.7588
>
> In this article, you fitted the model using the command:
>
> lme4::glmer(cbind(event,n-event)~factor(treat)+(control+treat-1|study), data=thedata1, family=binomial(link="logit"))
>
> Today, I found a page in your metafor webpage (http://www.metafor-project.org/doku.php/analyses:vanhouwelingen2002), fitting the same Model 6 mentioned above. However, you used metafor, not lme4 (of course), and the random effect structure seems a little bit different:
>
> res <- rma.mv(yi, vi, mods = ~ group - 1, random = ~ group | trial, struct="UN", data=dat.long, method="ML")
>
> Thus, I would like to first confirm if they are indeed the same model. If not, what are their differences and what would be major implications?
>
> Thank you very much,
>
> Arthur M. Albuquerque
>
> Medical student
> Universidade Federal do Rio de Janeiro, Brazil
>
> On Oct 18, 2021, 2:53 PM -0300, Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl>, wrote:
>> As far as I can tell, that seems to be Model 6: the "Van Houwelingen bivariate" model as discussed in our paper.
>>
>> Best,
>> Wolfgang
>>
>>> -----Original Message-----
>>> From: Arthur Albuquerque [mailto:arthurcsirio at gmail.com]
>>> Sent: Monday, 18 October, 2021 19:24
>>> To: r-sig-meta-analysis at r-project.org; Viechtbauer, Wolfgang (SP); Michael Dewey
>>> Subject: Re: [R-meta] Bivariate generalized linear mixed model with {metafor}
>>>
>>> Dear Michael,
>>>
>>> I?m sorry, my bad.
>>>
>>> It?s a binomial model with the logit link, in which the average baseline and
>>> treatment risks are treated as fixed effects. Moreover, there are two study-
>>> specific parameters (random-effects), and these are assumed to follow a bivariate
>>> normal distribution with covariance matrix ?E?. This matrix includes the?between-
>>> study variances for the baseline and treatment odds +??the correlation between
>>> the baseline and treatment risks in the logit scale.
>>>
>>> The authors then explain how to estimate marginal and conditional effects from
>>> this model using formulas. I am also not sure how to estimate these using
>>> metafor.
>>>
>>> They suggest using this model ?to include the baseline risk and report the
>>> variation in the effect measure with baseline risks in addition to the marginal
>>> effect, regardless of the measure of choice?.
>>>
>>> Sorry for the confusion, it?s my first time asking here and it is a quite
>>> complicated topic (at least for me).
>>>
>>> Best,
>>>
>>> Arthur M. Albuquerque
>>>
>>> Medical student
>>> Universidade Federal do Rio de Janeiro, Brazil
>>>
>>> On Oct 18, 2021, 2:10 PM -0300, Michael Dewey <lists at dewey.myzen.co.uk>, wrote:
>>>
>>> Dear Arthur
>>>
>>> You might get more helpful replies if you summarise the model for us
>>> rather than relying on someone here to do that for you.
>>>
>>> Michael
>>>
>>> On 18/10/2021 17:51, Arthur Albuquerque wrote:
>>>
>>> Dear Wolfgang,
>>>
>>> Thank you for the super quick reply! I wasn?t aware of that article, yet I
>>> believe it does not include the model I mentioned.
>>>
>>> The model is thoroughly described at the end of this article, section "Appendix
>>> B. The bivariate generalized linear mixed model
>>> (BGLMM)?:?https://doi.org/10.1016/j.jclinepi.2021.08.004
>>>
>>> Best,
>>>
>>> Arthur M. Albuquerque
>>>
>>> Medical student
>>> Universidade Federal do Rio de Janeiro, Brazil
>>>
>>> On Oct 18, 2021, 1:31 PM -0300, Viechtbauer, Wolfgang (SP)
>>> <wolfgang.viechtbauer at maastrichtuniversity.nl>, wrote:
>>>
>>> Dear Arthur,
>>>
>>> rma() does not fit generalized linear mixed models -- rma.glmm() does. I don't
>>> have the time right now to dig into those papers to figure out what specific
>>> model they are suggesting. In this context, many different models have been
>>> suggested; see, for example:
>>>
>>> Jackson, D., Law, M., Stijnen, T., Viechtbauer, W., & White, I. R. (2018). A
>>> comparison of seven random-effects models for meta-analyses that estimate the
>>> summary odds ratio. Statistics in Medicine, 37(7), 1059-1085.
>>> https://doi.org/10.1002/sim.7588
>>>
>>> (and this is not even an exhaustive list). The paper also indicates how these
>>> models can be fitted, either with metafor::rma.glmm() or one can do this directly
>>> with lme4""glmer().
>>>
>>> Best,
>>> Wolfgang
>>>
>>> -----Original Message-----
>>> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On
>>> Behalf Of Arthur Albuquerque
>>> Sent: Monday, 18 October, 2021 18:15
>>> To: r-sig-meta-analysis at r-project.org
>>> Subject: [R-meta] Bivariate generalized linear mixed model with {metafor}
>>>
>>> Hi all,
>>>
>>> I need some help to figure out how to fit a?bivariate generalized linear mixed
>>> model using metafor.
>>>
>>> In the past year, the Journal of Clinical Epidemiology has posted several
>>> articles on a controversy between using risk ratio or odds ratio in meta-
>>> analyses. Summary of the controversy here:
>>>
>>> George A. Wells , Commentary on Controversy and Debate 4 paper series:
>>> Questionable utility of the relative risk in clinical research, Journal of
>>> Clinical Epidemiology (2021), doi: https://doi.org/10.1016/j.jclinepi.2021.09.016
>>>
>>> One of the articles (https://doi.org/10.1016/j.jclinepi.2021.08.004) suggested
>>> fitting a bivariate generalized linear mixed model (BGLMM),??which "obtains
>>> effect estimates conditioning on baseline risks with the estimated model
>>> parameters, including the correlation parameter.?
>>>
>>> They fitted this model using the PROC NLMIXED command in SAS. I would like to fit
>>> this model using metafor, could anyone help me by sending the appropriate code of
>>> this model with metafor::rma()?
>>>
>>> Kind regards,
>>>
>>> Arthur M. Albuquerque
>>>
>>> Medical student
>>> Universidade Federal do Rio de Janeiro, Brazil
>
> [[alternative HTML version deleted]]
>
>
>
>
> ------------------------------
>
> Message: 2
> Date: Tue, 18 Jan 2022 08:56:00 +0100
> From: David Pedrosa <david.pedrosa at staff.uni-marburg.de>
> To: r-sig-meta-analysis at r-project.org
> Subject: [R-meta] Question on effect sizes
> Message-ID: <704e977a-ba10-4fed-bee9-8b4fcc37844e at Spark>
> Content-Type: text/plain; charset="utf-8"
>
> Dear list members,
> I currently have a comprehension question where I would like to ask for an assessment of the list. We are doing a meta-analysis where there are different expressions of outcomes that I am trying to combine using effect sizes.For the pre-post controlled tests, it looks something like this:
>
> +------------+-------------------+-------------+-------------------+-------------+
> | Study | Pre | | Post | |
> | # | Mean | SD | Mean | SD |
> | ===== = | ===========| =====================| =======|
> | 1 | Mean_x_y1 | SD_x_y1 | Mean_x_y2 | SD_x_y2 |
> | 2 | Mean_x1 | SD_x1 | Mean_x_x2 | SD_x2 |
> | 2 | Mean_y1 | SD_y1 | Mean_y2 | SD_y2 |
> +-------+----------------------+-------------------+---------------+-------------+
> My questions would be, if it resonable to assume that the pooled SD of the second study can be somehow estimated (unfortunately there is neither pretest data available nor a correlation between x and y)?
>
> And the other question would be a simple one which I could not find a definite answer for: How do I deal with studies indicating a mean change score, so how do I standardize Mean_change_x_y and SD_change_x_y in the scenario above when I don?t have a baseline score?
>
> Best wishes,
>
> [[alternative HTML version deleted]]
>
>
>
>
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> End of R-sig-meta-analysis Digest, Vol 56, Issue 16
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