[R-meta] Antw: RE: Network Meta-analysis metafor

[Viechtbauer Wolfgang (SP)]

I am not sure what exactly you are trying to do. But here is an example of a forest plot with a lot of customization:

http://www.metafor-project.org/doku.php/plots:forest_plot_with_subgroups

This won't directly address what you want to do, but it shows how one can create a plot for several subgroups.

Best,
Wolfgang

>-----Original Message-----
>From: Caroline Woehl [mailto:caroline.woehl at pg.hs-fulda.de]
>Sent: Thursday, 15 February, 2018 21:54
>To: Viechtbauer Wolfgang (SP)
>Cc: r-sig-meta-analysis at r-project.org
>Subject: Antw: RE: [R-meta] Network Meta-analysis metafor
>
>Dear Wolfgang,
>
>it works great, thanks a lot for that!
>
>I've already created a forest plot and calculated some subgroups, for
>example based on intervention duration or cognitive function at
>baseline. Could you give me some advice on how to add two subgroups in
>one forest plot? Is there any possibility?
>
>Thanks again for your help.
>
>Best regards,
>Caroline
>
>>>> "Viechtbauer Wolfgang (SP)"
><wolfgang.viechtbauer at maastrichtuniversity.nl> 14.02.18 13.16 Uhr >>>
>Dear Caroline,
>
>The example in help(dat.senn2013) uses mean differences to illustrate a
>network MA with metafor but is easy to adjust to standardized mean
>differences. For SMDs, you have to use a contrast-based model. The
>dat.senn2013 dataset is given in an arm-based format, so it first needs
>to be restructured. Note that Willms (1999) is the only multiarm study
>in this dataset, but the code should work regardless of how many
>multiarm studies there are. Just make sure that the 'reference'
>treatment for multiarm studies always goes under 'trt1'.
>
>library(metafor)
>
>#########################################################################
>###
>
>### load data
>dat <- get(data(dat.senn2013, package="metafor"))
>
>### restructure dataset to a contrast-based format
>dat <- dat.senn2013[,c(1,4,3,2,5,6)]
>dat.c <- lapply(split(dat, dat$study),
>                function(x) cbind(x[rep(1,nrow(x)-1),], x[-1,c(3:6)]))
>dat.c <- do.call(rbind, dat.c)
>names(dat.c)[3:10] <- c("trt1", "n1i", "m1i", "sd1i", "trt2", "n2i",
>"m2i", "sd2i")
>rownames(dat.c) <- 1:nrow(dat.c)
>dat.c$id <- 1:nrow(dat.c)
>dat <- dat.c
>rm(dat.c)
>
>#########################################################################
>###
>
>### make sure dataset is sorted by 'study'
>dat <- dat[order(dat$study),]
>
>### compute the total sample sizes of the studies
>dat$Ni <- unlist(lapply(split(dat, dat$study), function(x) rep(x$n1i[1]
>+ sum(x$n2i), each=nrow(x))))
>
>### compute the pooled SDs of the studies
>dat$sdpi <- unlist(lapply(split(dat, dat$study), function(x)
>sqrt(((x$n1i[1]-1)*x$sd1i^2 + sum((x$n2i-1)*x$sd2i^2)) / ((x$n1i[1] - 1)
>+ sum(x$n2i - 1)))))
>
>### calculate SMDs and corresponding sampling variances
>dat$yi <- with(dat, (m1i-m2i)/sdpi)
>dat$vi <- with(dat, 1/n1i + 1/n2i + yi^2/(2*Ni))
>
>### calculate the variance-covariance matrix of the SMDs for
>multitreatment studies
>calc.v <- function(x) {
>   v <- matrix(1/x$n1i[1] + outer(x$yi, x$yi, "*")/(2*x$Ni[1]),
>nrow=nrow(x), ncol=nrow(x))
>   diag(v) <- x$vi
>   v
>}
>V <- bldiag(lapply(split(dat, dat$study), calc.v))
>
>### convert trt1 and trt2 variables to factors and set levels
>lvls <- levels(factor(c(dat$trt1, dat$trt2)))
>dat$trt1 <- factor(dat$trt1, levels=lvls)
>dat$trt2 <- factor(dat$trt2, levels=lvls)
>
>### create variables to indicate the contrasts examined
>dat <- cbind(dat, model.matrix(~ dat$trt1 - 1) - model.matrix(~ dat$trt2
>- 1))
>names(dat)[(ncol(dat)-9):ncol(dat)] <- lvls
>
>#########################################################################
>###
>
>### network meta-analysis using a contrast-based random-effects model
>### by setting rho=1/2, tau^2 reflects the amount of heterogeneity for
>all treatment comparisons
>### the treatment left out (placebo) becomes the reference level for the
>treatment comparisons
>res <- rma.mv(yi, V, mods = ~ acarbose + benfluorex + metformin +
>miglitol + pioglitazone +
>                              rosiglitazone + sitagliptin + sulfonylurea
>+ vildagliptin - 1,
>              random = ~ factor(id) | study, rho=1/2, data=dat)
>res
>
>### estimate/test all pairwise differences between treatments
>### need 'multcomp' package for contrMat() function
>library(multcomp)
>contr <- contrMat(setNames(rep(1,res$p), colnames(res$X)), type="Tukey")
>sav <- predict(res, newmods=contr)
>sav[["slab"]] <- rownames(contr)
>sav$pval <- anova(res, L=contr)$pval
>sav
>
>#########################################################################
>###
>
>Two points:
>
>1) Note that I am using the pooled SD of all arms within each trial to
>compute the SMDs. That way, results within triIf you just use the pooled
>SD of each pairwise comparison, then this
>won't be the case.
>
>2) I see that you have the reference treatment listed under treat2. If
>you want to use the code above, just rename your variables to: study
>trt2 trt1 n2i m2i sd2i n1i m1i sd1i.
>
>Best,
>Wolfgang
>
>--
>Wolfgang Viechtbauer, Ph.D., Statistician | Department of Psychiatry and
>
>Neuropsychology | Maastricht University | P.O. Box 616 (VIJV1) | 6200 MD
>
>Maastricht, The Netherlands | +31 (43) 388-4170 | http://www.wvbauer.com
>
>>-----Original Message-----
>>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
>>project.org] On Behalf Of Caroline Woehl
>>Sent: Wednesday, 14 February, 2018 8:45
>>To: r-sig-meta-analysis at r-project.org
>>Subject: [R-meta] Network Meta-analysis metafor
>>
>>Dear all,
>>
>>I've only calucated network meta-analyses with the package netmeta yet.
>>Due to the possibility to calculate subgroup analyses, I'd like to have
>>a try at a network meta-analysis with metafor.
>>
>>The analysis is based on 20 randomized controlled trials. They compare
>>physical activity or cognitive activity with usual care. One of them is
>>a multi-arm trial comparing physical and cognitive activity with usual
>>care. I could extract sample size, mean und standard deviation
>>(posttreatment) from each study arm. Thus, the file contains the study,
>>treat1, treat2, n1i, m1i, sd1i, n2i, m2i and sd2i.
>>
>>Study                                                treat1
>treat2
>>        n1i          m1i      sd1i        n2i          m2i       sd2i
>>       1      physical activity    usual care     37   17,16    4,33
>>36   15,17    4,48
>>       2                    physical activity    usual care     39
>>19,40    8,68    35   18,48    8,40
>>       2                    cognitive activity   usual care    36
>>19,98    8,16    35   18,48    8,40
>>       3      cognitive activity usual care    35    42,34   9,30    33
>>  44,49   9,53
>>
>>So far, I’ve calculated the standardized mean differences (yi) and
>>variances (vi) for each pairwise comparison.
>>nma <- escalc(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i,
>>sd2i=sd2i, n2i=n2i, data=nma)
>>
>>How can I define usual care as the reference group in the next step?
>>
>>Many thanks for your help.
>>
>>Best regards,
>>Caroline