[R-meta] multi-level meta-analysis with dependent effect sizes

[Andrew Guerin]

I am new to meta-analysis, and have benefited greatly from the excellent documentation and resources for meta-analysis in R, especially the websites for metafor and clubSandwich. I am planning some extensive future work using meta-analysis, so thought it would be useful to get some experience of the whole process by starting with something relatively simple (haha) and trying to work it up for a publication. I would like some advice on whether I am following the correct approach.


I selected a topic with which I am quite familiar and which has a relatively limited literature: I did some thorough literature searching and came up with ~70 relevant studies, which I will use for a few models looking at different aspects (I'll focus on one here). Once I had coded all the data it became clear that the analysis was not going to be as simple as I had hoped, since the data have two kinds of dependence that should be accounted for:


1. Hierarchical dependence - a lot of the studies contribute multiple effect sizes.

2. Many of the effect sizes share controls - different treatments are compared against a common 'untreated' control - Gleser and Olkin's (2009) 'multiple treatments' dependence.


For this analysis, there are a total of 302 effect sizes, grouped into 244 'contrast' groups which share a control, from 43 studies. The 244 contrast groups contain 1-8 effect sizes.


There are various moderators. The most important is 'sampletype' - expected to be significant, as it is expected that treatment will have different effects on different sample types. The other factors all relate to variations in the experimental treatments used, to keep things general I'll just call them "A" to "H".


ndat = data frame

md = effect size measure (raw mean difference, since all data are on a common scale, with a limited range)

mdv = sampling variation for mean difference, obtained using escalc().

sid = study id (a simple numerical identifier)

contrast = identifier showing which effect size measures / treatments share a control (eg. study one contributes 5 effect sizes, with values of 'contrast' 1A, 1B, 1B, 1C, 1D, indicating that of the 5 effect sizes in study 1, 2 share a control).


As I see it I have the following options for how to proceed:


A) conduct a completely 'cluster naive' mixed effects meta-analysis


nmtA <- rma(md, mdv,  mods = ~ factor(sampletype) + factor(A) ... + factor(H), data=ndat, method="REML")

anova(nmtA, btt=20:21) ## example hypothesis test for moderator 'D'


B) ignore the hierarchical dependence (ie. assume that any samples with different values of 'contrast' within the same study are independent), but account for the correlated errors within clusters / contrast groups by RVE in robumeta, using clubSandwich for the hypothesis tests.


nmtB <- robu(md ~ factor(sampletype) + factor(A) ... + factor(H), data=ndat, var.eff.size=mdv, rho = 0.8, modelweights="CORR", studynum=contrast)

Wald_test(nmtB, constraints = 20:21, vcov="CR2") ## example hypothesis test for moderator 'D'



C) still ignoring the hierarchical dependence, but now explicitly specifying the random effects in rma.mv, after imputing a variance-covariance matrix. Robust parameter estimates and hypothesis test are then carried out using clubSandwich.


vcvndat <- impute_covariance_matrix(vi = ndat$mdv, cluster=ndat$contrast, r=0.7)

nmtC <- rma.mv(yi=md, V=vcvndat,  mods = ~ factor(sampletype) + factor(A) ... + factor(H), random = ~1|contrast, data=ndat, method="REML")

nmtC_robust <- coef_test(nmtC, vcov="CR2")

Wald_test(nmtC, constraints = 20:21, vcov="CR2") ## example hypothesis test for moderator 'D'


D) attempting to account for the hierarchical and multiple-treatments dependence via a multi-level model, setting random effect structure as random = ~ 1| sid/contrast, and then using clubSandwich for RVE and hypothesis tests.


vcvndat <- impute_covariance_matrix(vi = ndat$mdv, cluster=ndat$contrast, r=0.7)

nmtD <- rma.mv(yi=md, V=vcvndat,  mods = ~ factor(sampletype) + factor(A) ... + factor(H), random = ~1|sid/contrast, data=ndat, method="REML")

nmtD_robust <- coef_test(nmtD, vcov="CR2", cluster=ndat$contrast)

Wald_test(nmtD, constraints = 20:21, vcov="CR2", cluster=ndat$contrast) ## example hypothesis test for moderator 'D'


My main question really is which (if any) of these approaches is the best way forward? For this particular dataset, the outcomes (at least in terms of which moderators are significant/non-significant, and most parameter estimates) are pretty similar for all the options (including some other options that I tried that I have not mentioned here). All find 'sampletype' highly significant, and find similar sets of the other moderators to be non-significant (except for the naive analysis, A which finds more moderators to be significant). B, C, and D are all very similar. Although D is the only one that explicitly accounts for the hierarchical dependence, does the fact that the outcomes from B and C are substantially similar mean that this could be safely overlooked for this analysis?


I have not yet attempted to build my own vcov matrix using the Gleser and Olkin (2009) formula. Is this worth doing instead of using impute_covariance_matrix? I picked 0.7 as a plausible value for r in impute_convariance_matrix(), but I have raw data from one study that might allow me to derive a better estimate. I did investigate the effect of varying r from 0-1. It does not seem to have much impact on parameter estimates, the significance/non-significance of moderators, or the estimate of sigma^2.2 in the multi-level model. The intercept moves about a little, but the main effect is that when r is less than about 0.5, sigma^2.1 is basically 0. Is this indicating a problem?


Finally I was wondering whether it is wise to account for multiple comparisons when evaluating the hypothesis tests. Since I have several factors, I repeat Wald_test() several times. Models nmtB, nmtC, and nmtD all have moderators  (aside from 'sampletype') that are significant at p < 0.05, but with p values only just under 0.05. Adjustment for multiple comparisons (eg. using p.adjust(method="fdr")) shifts the outcomes of all these tests to p > 0.05.


I would be grateful for any advice on the above.


Many thanks,

Andrew





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