[R-meta] Computing var-covariance matrix with correlations of six non-independent outcomes
Mika Manninen
m|xu89 @end|ng |rom gm@||@com
Wed Jul 8 10:32:31 CEST 2020
Wolfgang,
Wow! That is about as much detail as one could ever hope for, thank you so
much again. That clarifies a lot, not least that the recommendation for the
multilevel models is not always based on a deep understanding of the
assumptions and data relationships. but rather seems to be almost like a
trendy word.
Mika
ps hope to take part in your meta-analysis workshops in the future!
ke 8. heinäk. 2020 klo 11.20 Viechtbauer, Wolfgang (SP) (
wolfgang.viechtbauer using maastrichtuniversity.nl) kirjoitti:
> Hi Mika,
>
> Just for completeness sake, there was one omission and one error in the
> code I posted:
>
> meta <- data.frame(meta) # to turn your matrix into a dataframe
>
> And this line should have been:
>
> corlist <- mapply(function(rmat, mots) rmat[mots,mots], corlist, mots)
>
> As for your questions:
>
> 1) Running separate analyses: Nothing inherently wrong with that. But
> there is the potential to obtain slightly more precise estimates of the 6
> average effects by 'borrowing information' from each other. Using the
> subset of the data you posted, let's compare:
>
> # multivariate model
> res <- rma.mv(g, V, mods = ~ factor(motivation) - 1, random = ~
> factor(motivation) | study, struct="UN", data=meta)
> res
>
> # separate RE models for each level of motivation
> sep <- lapply(1:6, function(i) rma(g, v, data=meta, subset=motivation==i))
> sep <- do.call(rbind, lapply(sep, function(x) cbind(coef(summary(x)),
> tau2=x$tau2)))
> rownames(sep) <- paste0("motivation", 1:6)
>
> # compare results
> res
> round(sep, 4)
>
> As you will see, the SE of several estimates (but not all) is lower in the
> MV model compared to the separate models.
>
> Also, if you want to compare effects, you really need the MV model. For
> example, I can do this to test whether the effect is different for outcomes
> 1 and 2:
>
> anova(res, L=c(1,-1,0,0,0,0))
>
> But you cannot do this when you have fitted separate models, because the
> results in sep[[1]] and sep[[2]] are not independent.
>
> 2) Why do people propose fitting a multilevel model (with random effects
> for study and estimates within studies)?
>
> I can only speculate about this. I suspect it is a combination of the
> following things.
>
> For one, people find it difficult to assemble the correlations needed to
> fit a proper multivariate model (so that the V matrix can be constructed).
> Also, this step is not something that is easily automated and requires some
> programming.
>
> Also, people may have learned in their multilevel course that one can deal
> with dependencies by adding appropriate random effects to their model. That
> is true to some extent, but there are two levels of dependencies in the
> present (i.e., meta-analytic) context, namely dependencies between the
> sampling errors (which are captured by the covariances in the V matrix) and
> dependencies between the underlying true effects (which we can capture by
> adding correlated random effects within studies to the model). People may
> not be aware of this additional complexity and hence may think that just
> adding random effects is sufficient.
>
> Furthermore, there are some papers that discuss the use of the multilevel
> approach for meta-analyzing correlated outcomes:
>
> Moeyaert, M., Ugille, M., Beretvas, S. N., Ferron, J., Bunuan, R., & Van
> den Noortgate, W. (2017). Methods for dealing with multiple outcomes in
> meta-analysis: A comparison between averaging effect sizes, robust variance
> estimation and multilevel meta-analysis. International Journal of Social
> Research Methodology, 20(6), 559-572.
>
> Van den Noortgate, W., Lopez-Lopez, J. A., Marin-Martinez, F., &
> Sanchez-Meca, J. (2013). Three-level meta-analysis of dependent effect
> sizes. Behavior Research Methods, 45(2), 576-594.
>
> Van den Noortgate, W., Lopez-Lopez, J. A., Marin-Martinez, F., &
> Sanchez-Meca, J. (2015). Meta-analysis of multiple outcomes: A multilevel
> approach. Behavior Research Methods, 47(4), 1274-1294.
>
> Based on the simulations, the multilevel approach may be 'good enough'
> under certain conditions.
>
> In the present example, that would be:
>
> mlm <- rma.mv(g, v, mods = ~ factor(motivation) - 1, random = ~ 1 |
> study/outcome, data=meta)
> mlm
>
> There are three essential differences compared to the multivariate model
> we fitted earlier:
>
> a) We ignore the covariances in V and treat the sampling errors as
> independent.
> b) The three-level model implies that the correlation of the underlying
> true effects within studies is the same for all 6*5/2 = 15 pairs of
> outcomes.
> c) The model implies that the amount of heterogeneity is the same for all
> 6 outcomes.
>
> Since the study-level variance component is estimated to be 0 here, the
> model actually implies that there is no correlation between the true
> effects for the different outcomes within studies.
>
> Therefore, the results would be the same as fitting 6 separate models
> where we constrain the amount of heterogeneity to the outcome-level
> variance component. We can easily check this:
>
> sep <- lapply(1:6, function(i) rma(g, v, data=meta, subset=motivation==i,
> tau2=mlm$sigma2[2]))
> sep <- do.call(rbind, lapply(sep, function(x) cbind(coef(summary(x)),
> tau2=x$tau2)))
> rownames(sep) <- paste0("motivation", 1:6)
> round(sep, 4)
>
> You will see that the results are indeed exactly the same as those from
> the multilevel model.
>
> Nobody can say which result is "correct" here, but the multilevel model
> makes a number of simplifying assumptions that I would want to avoid if
> possible.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: Mika Manninen [mailto:mixu89 using gmail.com]
> >Sent: Wednesday, 08 July, 2020 9:25
> >To: Viechtbauer, Wolfgang (SP)
> >Cc: James Pustejovsky; r-sig-meta-analysis using r-project.org
> >Subject: Re: [R-meta] Computing var-covariance matrix with correlations of
> >six non-independent outcomes
> >
> >Dear Wolfgang and James,
> >
> >Thank you so much for your replies and I just want to say that I can only
> >admire your attitude and willingness to help and explain. I was banging my
> >head against the wall for a while there trying to figure out how the
> matrix
> >should be created. I owe you one!
> >
> >Two quick questions. The multivariate approach with the var-cov matrix
> seems
> >to make the most sense for this analysis, but if one was to run six
> separate
> >analyses for the outcomes (like I did the first time around), how
> >problematic would that be ? Also, can you estimate as to why a 3-level
> >approach was recommended to me (just adding hierarchical study level to
> the
> >analysis)? It is hard for me to see the rationale behind this.
> >
> >I hope you have a great day,
> >
> >Mika
> >
> >ti 7. heinäk. 2020 klo 23.45 Viechtbauer, Wolfgang (SP)
> >(wolfgang.viechtbauer using maastrichtuniversity.nl) kirjoitti:
> >I swear that James and I did not coordinate our responses. But this is
> >seriously scary how similar our responses were.
> >
> >>-----Original Message-----
> >>From: James Pustejovsky [mailto:jepusto using gmail.com]
> >>Sent: Tuesday, 07 July, 2020 22:41
> >>To: Viechtbauer, Wolfgang (SP)
> >>Cc: Mika Manninen; r-sig-meta-analysis using r-project.org
> >>Subject: Re: [R-meta] Computing var-covariance matrix with correlations
> of
> >>six non-independent outcomes
> >>
> >>Ha! Following best practice for systematic reviews, a response to your
> >>question has been provided by two coders, working independently. I think
> >our
> >>reliability is pretty decent too.
> >>
> >>On Tue, Jul 7, 2020 at 3:33 PM Viechtbauer, Wolfgang (SP)
> >><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> >>Dear Mika,
> >>
> >>Strictly speaking, the correct way to compute the covariances between
> >>multiple Cohen's d (or Hedges' g) values would require using methods that
> >>are described in this chapter:
> >>
> >>Gleser, L. J., & Olkin, I. (2009). Stochastically dependent effect sizes.
> >In
> >>H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of
> research
> >>synthesis and meta-analysis (2nd ed., pp. 357–376). New York: Russell
> Sage
> >>Foundation.
> >>
> >>In particular, equation (19.27) gives the covariance between two d values
> >>for two different outcomes computed based on the same sample of subjects
> >>(and we assume that the correlation between the two outcomes is the same
> >for
> >>subjects in the treatment and the control group). An example illustrating
> >an
> >>application of this is provided here:
> >>
> >>http://www.metafor-project.org/doku.php/analyses:gleser2009#multiple-
> >>endpoint_studies
> >>
> >>although the code for the example is a bit simpler because each study has
> >>exactly 2 d values.
> >>
> >>However, one can take a simpler approach that gives very similar results.
> >>Namely, if v_1 and v_2 are the sampling variances of d_1 and d_2 and r is
> >>the correlation between the two outcomes, then r * sqrt(v_1 * v_2) is
> >>usually a close approximation to what (19.27) would provide.
> >>
> >>Based on your example, this can be computed here as follows:
> >>
> >># this is only required because you coded missings as "NA" (and not as
> NA)
> >>corlist <- lapply(corlist, function(x) matrix(as.numeric(x), nrow=6,
> >>ncol=6))
> >>
> >># we only need a correlation matrix for the levels of motivation actually
> >>observed
> >>mots <- split(meta$motivation, meta$study)
> >>corlist <- mapply(function(rmat, mots) R[mots,mots], corlist, mots)
> >>corlist
> >>
> >>Vfun <- function(vi,rmat) {
> >> if (length(vi) == 1L) {
> >> matrix(vi)
> >> } else {
> >> S <- diag(sqrt(vi))
> >> S %*% rmat %*% S
> >> }
> >>}
> >>
> >>V <- mapply(Vfun, split(meta$v, meta$study), corlist)
> >>
> >>Now V is the var-cov matrix of your SMD values, so you could proceed
> with a
> >>multivariate model with:
> >>
> >>res <- rma.mv(g, V, mods = ~ factor(motivation) - 1, random = ~
> >>factor(motivation) | study, struct="UN", data=meta)
> >>res
> >>
> >>This is a very general model allowing each level of motivation to have
> its
> >>own heterogeneity variance component and each pair to have its own
> >>correlation. I wouldn't recommend this with only 8 studies, but with 25
> >>studies, this might be more sensible.
> >>
> >>Best,
> >>Wolfgang
> >>
> >>>-----Original Message-----
> >>>From: Mika Manninen [mailto:mixu89 using gmail.com]
> >>>Sent: Tuesday, 07 July, 2020 21:53
> >>>To: James Pustejovsky
> >>>Cc: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using r-project.org
> >>>Subject: Re: [R-meta] Computing var-covariance matrix with correlations
> of
> >>>six non-independent outcomes
> >>>
> >>>James,
> >>>
> >>>Thank you so much for replying.
> >>>
> >>>The dataset (8 studies out of 25 included) can be created with the
> >>following
> >>>code:
> >>>
> >>>###Creating vectors for the outcome, study, motivation, effect size, and
> >>>variance estimate
> >>>
> >>>#Total effects/outcomes
> >>>outcome <- c(1:28)
> >>>
> >>>#Study
> >>>study <- c(1,1,1,1,1,2,2,2,2,3,3,3,3,3,4,4,4,4,4,5,6,7,8,8,8,8,8,8)
> >>>
> >>>#Six different motivation types measured in the studies
> >>>motivation <-
> >>>c(1,3,4,5,6,1,3,4,5,1,3,4,5,6,1,3,4,5,6,6,6,6,1,2,3,4,5,6)
> >>>
> >>>#Hedges g:s
> >>>g <- c(0.6068, 0.0603, 0.2684, -0.0886, 0.0415, 1.592, 1.4031, 0.7928,
> >>>0.2013, 0.541, 0.1169,
> >>> 0.3129, -0.0275, -0.3536, 1.5886, 2.7218, -1.6273, -0.4375, -
> >1.0695,
> >>>-0.1247, -0.1038,
> >>> -0.2706, 0.2045, -0.2701, 0.3763, -0.7371, -0.0666, 0.2789)
> >>>
> >>>#Variance estimates
> >>>v <- c( 0.0162, 0.0155, 0.0157, 0.0155, 0.0155, 0.1889,
> >>>0.17875984,0.15484225,
> >>> 0.14432401, 0.0329, 0.0318, 0.0322, 0.0318, 0.0323, 0.1886,
> >0.2758,
> >>> 0.1909, 0.147, 0.164, 0.0067, 0.0028, 0.004, 0.0726, 0.0729,
> >>0.0735,
> >>> 0.0771, 0.0723, 0.073)
> >>>
> >>>###Dataset matrix with different levels and motivation types
> >>>meta <- cbind(outcome, study, motivation, g, v)
> >>>View(meta)
> >>>
> >>>I created 6x6 correlation matrices (with 15 unique correlations) for the
> >>>eight included studies. I am not sure if this is the most sensible
> >approach
> >>>as only a few studies have measured all six outcomes. Value NA reflects
> >the
> >>>absence of a correlation (absence of that one or both of the motivation
> >>>types in the study). The row and column numbers (1-6) correspond to the
> >>>variable motivation in the created dataset.
> >>>
> >>>### Correlation matrices for studies 1-8
> >>>
> >>>study1c <- rbind(c(1, "NA", .96, .34, -.33, -.66), c("NA", "NA", "NA",
> >>"NA",
> >>>"NA", "NA"), c(.96, "NA", 1, .55, -.12, -.50),
> >>> c(.34, "NA", .55, 1, .53, .05), c(-.33, "NA", -.12,
> .53,
> >>1,
> >>>.72), c(-.66, "NA", -.50, .05, .72, 1))
> >>>
> >>>study2c <- rbind(c(1, "NA", .96, .34, -.33, "NA"), c("NA", "NA", "NA",
> >>"NA",
> >>>"NA", "NA"), c(.96, "NA", 1, .55, -.12, "NA"),
> >>> c(.34, "NA", .55, 1, .53, "NA"), c(-.33, "NA", -.12,
> .53,
> >>1,
> >>>"NA"), c("NA", "NA", "NA", "NA", "NA", "NA"))
> >>>
> >>>study3c <- rbind(c(1, "NA", .96, .34, -.33, -.66), c("NA", "NA", "NA",
> >>"NA",
> >>>"NA", "NA"), c(.96, "NA", 1, .55, -.12, -.50),
> >>> c(.34, "NA", .55, 1, .53, .05), c(-.33, "NA", -.12,
> .53,
> >>1,
> >>>.72), c(-.66, "NA", -.50, .05, .72, 1))
> >>>
> >>>study4c <- rbind(c(1, "NA", .85, .35, .06, -.44), c("NA", "NA", "NA",
> >"NA",
> >>>"NA", "NA"), c(.85, "NA", 1, .59, .46, -.14),
> >>> c(.35, "NA", .59, 1, .71, .27), c(.06, "NA", .46, .71,
> 1,
> >>>.52), c(-.44, "NA", -.14, .27, .52, 1))
> >>>
> >>>study5c <- rbind(c("NA", "NA", "NA", "NA", "NA", "NA"), c("NA", "NA",
> >"NA",
> >>>"NA", "NA", "NA"), c("NA", "NA", "NA", "NA", "NA", "NA"),
> >>> c("NA", "NA", "NA", "NA", "NA", "NA"), c("NA", "NA",
> "NA",
> >>>"NA", "NA", "NA"), c("NA", "NA", "NA", "NA", "NA", 1))
> >>>
> >>>study6c <- rbind(c("NA", "NA", "NA", "NA", "NA", "NA"), c("NA", "NA",
> >"NA",
> >>>"NA", "NA", "NA"), c("NA", "NA", "NA", "NA", "NA", "NA"),
> >>> c("NA", "NA", "NA", "NA", "NA", "NA"), c("NA", "NA",
> "NA",
> >>>"NA", "NA", "NA"), c("NA", "NA", "NA", "NA", "NA", 1))
> >>>
> >>>study7c <- rbind(c("NA", "NA", "NA", "NA", "NA", "NA"), c("NA", "NA",
> >"NA",
> >>>"NA", "NA", "NA"), c("NA", "NA", "NA", "NA", "NA", "NA"),
> >>> c("NA", "NA", "NA", "NA", "NA", "NA"), c("NA", "NA",
> "NA",
> >>>"NA", "NA", "NA"), c("NA", "NA", "NA", "NA", "NA", 1))
> >>>
> >>>study8c <- rbind(c(1, .79, .84, .24, -.20, -.50), c(.79, 1, .93, .47,
> .00,
> >>-
> >>>.31), c(.84, .93, 1, .57, -.07, -.52),
> >>> c(.24, .47, .57, 1, .43, .01), c(-.20, .00, -.07,
> .43,
> >>>1, .55), c(-.50, -.31, -.52, .01, .55, 1))
> >>>
> >>>#list with all the correlations matrices
> >>>
> >>>corlist <- list(study1c, study2c, study3c, study4c, study5c, study6c,
> >>>study7c, study8c)
> >>>
> >>>Mika
> >>>
> >>>ti 7. heinäk. 2020 klo 19.54 James Pustejovsky (jepusto using gmail.com)
> >>>kirjoitti:
> >>>Hi Mika,
> >>>
> >>>To add to Wolfgang's question, could you tell us a little bit more about
> >>how
> >>>you have structured the data on correlations between types of
> motivation?
> >>Is
> >>>it just one correlation matrix (6X6 matrix, with 1 + 2 + 3 + 4 + 5 = 15
> >>>unique correlations)? Or is it study-specific?
> >>>
> >>>This sort of calculation is a bit tricky to carry out so I am not
> >surprised
> >>>that you haven't found a solution in the listserv archives. If you are
> >>>willing to share your dataset (or a subset thereof, say 3-4 studies
> worth
> >>of
> >>>data), it may make it easier for us to help problem solve.
> >>>
> >>>James
> >>>
> >>>On Tue, Jul 7, 2020 at 11:42 AM Viechtbauer, Wolfgang (SP)
> >>><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> >>>Dear Mika,
> >>>
> >>>What effect size measure are you using for the meta-analysis?
> >>>
> >>>Best,
> >>>Wolfgang
> >>>
> >>>>-----Original Message-----
> >>>>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-
> >>>project.org]
> >>>>On Behalf Of Mika Manninen
> >>>>Sent: Tuesday, 07 July, 2020 18:10
> >>>>To: r-sig-meta-analysis using r-project.org
> >>>>Subject: [R-meta] Computing var-covariance matrix with correlations of
> >six
> >>>>non-independent outcomes
> >>>>
> >>>>Hello,
> >>>>
> >>>>I am doing a meta-analysis looking at the effect of a teaching
> >>intervention
> >>>>(versus control) on six types of motivation/behavioral regulation.
> >>>>Theoretically and empirically these constructs form a continuum in
> which
> >>>>the continuum neighbors are most strongly positively correlated and the
> >>>>furthest from one another most negatively correlated.
> >>>>
> >>>>I have 95 effects. These effects come from 25 studies, each reporting
> >>>>scores for between 1-6 motivation types. The number of effects per
> >>>>motivation ranges from 22 to 13. In some studies, they have measured
> only
> >>>>one or two types whereas in others they have measured 5 or all 6 types
> of
> >>>>motivation.
> >>>>
> >>>>I originally ran a separate random-effects meta-analysis for all the
> six
> >>>>outcomes. However, I got feedback that the dependency of the motivation
> >>>>types should be taken into account and a 3-level meta-analysis was
> >>>>recommended. After looking into it, the 3-level model seems to be a
> worse
> >>>>approach than the multivariate approach.
> >>>>
> >>>>As is not usually the case, I have succeeded in gathering all
> >correlations
> >>>>between all the motivation types for all studies (some from original
> >>>>reporting and some from previous meta-analysis findings).
> >>>>
> >>>>My question is, how do I compute the V-matrix for this data in order to
> >>run
> >>>>the multivariate analysis? I read the whole archive but I could not
> find
> >a
> >>>>clear answer to the problem.
> >>>>
> >>>>Thank you very much in advance,
> >>>>
> >>>>Mika
>
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