[R-meta] Variance Components from Multiple Imputation of Missing Moderator Variables in rma.mv

Fri, Mar 5, 2021 12:21 AM

Dear Denise,

For the variance components, you could just take their average across the different model fits.

As for pooling multiple heterogeneity tests: These are chi^2 tests and I am not sure what the 'standard' procedures are for pooling such tests. A quick search turned up this article:

https://www.jstor.org/stable/24303994

but what is described there isn't entirely trivial.

I also came across:

https://link.springer.com/article/10.1186/s12874-017-0404-7

which discusses the issue in a different context, but suggests a rather simple solution, namely taking the median of the p-value from the different tests. At least in the context of this article, that seems to work well. I cannot tell you to what extent the results from that paper would apply to the present context, but taking the median of the p-values is an amazingly simple solution :)

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of brauldeq
Sent: Wednesday, 03 March, 2021 11:28
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Variance Components from Multiple Imputation of Missing
Moderator Variables in rma.mv

Dear fellow researchers,

I am working on a meta-regression of the relationship between
concientiousness and self-efficacy.  I included every reported effect
size, with some studies contributing more than one effect size. As my
data is structured hierarchically, I used a three-level mixed-effects
model for analysis. The R code is as follows:

rma.mv(y.c, v.c,  random = ~ 1 | sample_nr/effect_nr,
                      mods = ~ year + interval.months + idv + class + se
+ pers_rater + pers_retro,
                      method="REML", data=con_se, digits = 3 )

The moderators of interest are year of publication (year), interval
between measurements (interva.months), grade level of participants
(anything from class 1 to 13), Some of the moderators have missing
values (interval.months and class, both of which are continuous
variables). The effect size of interest is correlation coefficients. I
tried to impute the missing moderator values via multiple imputation
with the mice package.

I basically followed the instructions on:
https://www.metafor-
project.org/doku.php/tips:multiple_imputation_with_mice_and_metafor?s[]=multiple&s
[]=imputation

I the following code to impement missing moderator values:

withold <- function (data, expr) {
  call <- match.call()
  analyses <- as.list(seq_len(data$m))
  for (i in seq_along(analyses)) {
    data.i <- complete(data, i)
    analyses[[i]] <- eval(expr = substitute(expr), envir = data.i,
enclos = parent.frame())
    if (is.expression(analyses[[i]]))
      analyses[[i]] <- eval(expr = analyses[[i]], envir = data.i, enclos
= parent.frame())
  }
  object <- list(call = call, call1 = data$call, nmis = data$nmis,
analyses = analyses)
  oldClass(object) <- c("mira", "matrix")
  object
}

#set up predictor matrix
predMatrix <- make.predictorMatrix(con_se1)

#predMatrix[,"v"] <- 0 # don't use vi for imputing
#predMatrix["y",] <- 0 # don't impute yi (since yi has no NAs, this is
actually irrelevant here)
#predMatrix["v",] <- 0 # don't impute vi (since vi has no NAs, this is
actually irrelevant here)
predMatrix[,"effect_nr"] <- 0 # don't use effect number for imputing
predMatrix["effect_nr",] <- 0 # don't impute effect number (since effect
number has no NAs, this is actually irrelevant here)
predMatrix

# set up vector which specifies prediction method
impMethod <- make.method(con_se1)
#impMethod

# impute missing values
imp <- mice(con_se1, print=FALSE, m=20, predictorMatrix=predMatrix,
method=impMethod, seed=1234)

# fit meta-regression with imputed values
con_se.fit <- withold(imp, rma.mv(y.c, v.c,
                      random = ~ 1 | sample_nr/effect_nr,
                      mods = ~ year + interval.months + idv + class + se
+ pers_rater + pers_retro,
                      method="ML", data=con_se1, digits = 3
                      ))

# pool and round results
con_se.mod.mi <- summary(pool(con_se.fit))
con_se.mod.mi[-1] <- round(con_se.mod.mi[-1], digits=3)
con_se.mod.mi

Now beta coefficients of moderator variables (with missing values) were
successfully pooled. However, I did not find a way to estimate
heterogeneity statistics and variance components. Is there a way to pool
the results from multiple variance estimations and (residual)
heterogeneity tests as well?

I would be extremely thankful for any thoughts and tips!

Best regards,
Denise Braul

[R-meta] Variance Components from Multiple Imputation of Missing Moderator Variables in rma.mv

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