[R-meta] Adjusted effects p- and z-value

[Viechtbauer, Wolfgang (NP)]

Yes, this also works for this case. Note that model.matrix() already includes the intercept column, so there is no need to remove it (with [-1]) and then manually add it back. So this should work:

anova(res, X=colMeans(model.matrix(categorical_moderator)))

(since the mean of the columns of 1s is just 1).

As for citing things from the metafor website - just follow whatever the rules are for the citation format you need to use. For example, with APA style, I think it would be:

Viechtbauer, W. (2022, April 22). Computing Adjusted Effects Based on Meta-Regression Models. The metafor Package: A Meta-Analysis Package for R. https://www.metafor-project.org/doku.php/tips:computing_adjusted_effects

The date is the one shown at the bottom (Last modified: 2022/04/22). No idea what part needs to be in italics here (the webpage title or the website title), if any.

Best,
Wolfgang

>-----Original Message-----
>From: Lena Pollerhoff [mailto:lena using pollerhoff.de]
>Sent: Thursday, 07 July, 2022 14:23
>To: Viechtbauer, Wolfgang (NP)
>Cc: r-sig-meta-analysis using r-project.org
>Subject: Re: [R-meta] Adjusted effects p- and z-value
>
>Dear Wolfgang,
>
>Thank you so much for your quick response!
>
>I assume it is also possible to calculate the p- and z-values with the anova
>function for the mean of a dummy variable in case of a categorical predictor,
>e.g. anova(res,X=c(1,colMeans(model.matrix(categorical_moderator))[-1])) ?
>
>Further what would the best way to cite your online tutorials like the one for
>the adjusted effects?
>
>Best wishes
>Lena
>
>> Am 07.07.2022 um 10:14 schrieb Viechtbauer, Wolfgang (NP)
><wolfgang.viechtbauer using maastrichtuniversity.nl>:
>>
>> Hi Lena,
>>
>> See below for my responses.
>>
>> Best,
>> Wolfgang
>>
>>> -----Original Message-----
>>> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org]
>On
>>> Behalf Of Lena Pollerhoff
>>> Sent: Thursday, 07 July, 2022 8:01
>>> To: r-sig-meta-analysis using r-project.org
>>> Subject: [R-meta] Adjusted effects p- and z-value
>>>
>>> Hey everyone,
>>>
>>> I’m currently computing adjusted effects based on meta-regression models
>(based
>>> on this tutorial https://www.metafor-
>>> project.org/doku.php/tips:computing_adjusted_effects <https://www.metafor-
>>> project.org/doku.php/tips:computing_adjusted_effects>).
>>> When I am computing the effect with predict(res, newmods = mean(continuous
>>> variable)) I receive the estimated average effect size (pred), the CI and PI.
>>> Now I was wondering whether it would be also possible to receive the p-value
>and
>>> z-value for the adjusted effect (similar to the output of a random-effect
>model)?
>>
>> You can use anova() for this. Using the same example from the tutorial:
>>
>> predict(res, newmods = mean(dat$ablat), transf=exp, digits=2)
>>
>> anova(res, X = c(1,mean(dat$ablat)))
>>
>> In contrast to predict() (where the 'intercept' argument controls whether the
>intercept is included in the linear contrast), with anova() this is done as part
>of X. Also, there is no 'transf' argument for anova(), but this is not relevant
>anyway, since the testing is done on the original (in this case, log) scale.
>>
>>> Further I was wondering whether it is possible to include the PI in the forest
>>> plot (as the dotted lines around the diamond shaped effect) when I am manually
>>> including the adjusted effect in the forest plot with e.g.,
>>> sav <- predict(res, newmods = mean(continuous variable))
>>> Addpoly(sav$pred, sei=sav$se)
>>>
>>> Normally one could add it with addpred = TRUE, but this does not work in this
>>> specific case.
>>
>> There is actually an addpoly() method for 'predict.rma' objects now. So you can
>do:
>>
>> pred <- predict(res, newmods = mean(dat$ablat))
>> addpoly(pred, addpred=TRUE)
>>
>> For this to work, don't use transf=exp in predict(). If transf=exp was used as
>part of the call to forest(), then the addpoly() method will automatically use
>the same transformation automatically.
>>
>> dat <- dat.bcg
>> dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat,
>>              slab=paste0(author, ", ", year))
>>
>> res <- rma(yi, vi, data=dat)
>> forest(res, xlim=c(-6.8,3.8), header=TRUE, top=2, atransf=exp, cex=0.9,
>addpred=TRUE,
>>       at=log(c(1/16, 1/8, 1/4, 1/2, 1, 2, 4, 8)), digits=c(2L,4L), ylim=c(-
>2,15))
>> res <- rma(yi, vi, mods = ~ ablat, data=dat)
>> pred <- predict(res, newmods = mean(dat$ablat))
>> addpoly(pred, addpred=TRUE)
>>
>> I need to update the tutorial to show this.
>>
>>> Thanks in advance and best wishes
>>> Lena