# [R-meta] Specifying the random effect structure of our multilevel meta-analysis in metafor

Tomas-Valiente Jorda Francisco tom@@| @end|ng |rom @tudent@ethz@ch
Mon Jan 23 12:05:21 CET 2023

```Hello,

I am part of a team, with Prof Peter John at King�s College London and Dr Florian Foos and Ceren Cinar at the London School of Economics, that is working on a meta-analysis of get-out-the-vote (GOTV) interventions. Our project looks at whether GOTV�s effect is larger on people with low vs high voting propensity. We had a question about how to specify the random effect�s structure of our model.

Our setup is as follows. We have estimates for the ITT of many GOTV interventions, computed comparing turnout between individuals randomized to some treatment group and a control group. Importantly, for each treatment, we have effect estimates separately for individuals with high vs medium vs low voting propensities. Many experiments have multiple treatment arms (which often differ on the particular get-out-the-vote message used), so effectively we have effect estimates for each voting propensity-experiment-arm triplet, with arms nested within experiments. Since we have subjects of each voting propensity on (almost) all arms, for any experiment with (say) two treatment arms, we have an estimate of control vs treatment 1 and control vs treatment 2 for each voting propensity separately. Using simulated data, our dataset has the following structure:

df <- structure(list(effect = c(0.155803932130353, 0.172093700115289, 0.228564716936809, -0.0029411764705886, 0.226006191950464, 0.24365585512081, 0.205042523718298, 0.529411764705883, 0.418300653594771, 0.333333333333333, 0.357142857142857, 0.333333333333333, 0.227386316387913, 0.110813804839315, -0.0158629160414587, 0.362962962962963, 0.266666666666667, 0.127272727272728), arm = structure(c(1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("1", "2", "3"), class = "factor"), experiment = c("A", "A", "A", "B", "B", "B", "B", "B", "B", "C", "C", "C", "C", "C", "C", "C", "C", "C"), voting_prop = c("low", "medium", "high", "low", "low", "medium", "medium", "high", "high", "low", "low", "low", "medium", "medium", "medium", "high", "high", "high")), row.names = c(NA, -18L), class = "data.frame")

Sampling errors are not independent across arms within voting propensity-experiment pairs, since the same control group (which is specific to the voting propensity-experiment pair) is used to estimate the ITT of different treatment arms. We use the Gleser & Olkin (2009) method to estimate the variance-covariance matrix of sampling errors, which for the example above is:

V <- structure(c(0.00527759653574149, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.000272246907504789, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0012451834193348, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0268238335114407,
0.0144993694656436, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.0144993694656436, 0.0274724895138511, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000219964986797141,
0.000110094264225202, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.000110094264225202, 0.000220356782547541, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0227385071675729,
0.0084216693213233, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.0084216693213233, 0.0163754681247953, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0523485584710074, 0.0294460641399417,
0.0294460641399417, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.0294460641399417, 0.0441690962099125, 0.0294460641399417,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0294460641399417,
0.0294460641399417, 0.0523485584710074, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000477394970026329, 0.00023697669984828,
0.000236976699848284, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.00023697669984828, 0.000475661779613111, 0.000236976699848285,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000236976699848284,
0.000236976699848285, 0.000476154421676615, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0147396599742279, 0.00947549569771795,
0.00947549569771793, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.00947549569771795, 0.0189509913954359, 0.00947549569771794,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00947549569771793,
0.00947549569771794, 0.015936060946162), .Dim = c(18L, 18L))

We are interested in testing whether the effect of get-out-the-vote interventions differs across voting propensities. We are planning to use the metafor package. But we are not sure of what would be a reasonable way to specify the random effect structure of the model to properly capture how true effects are correlated. We think the best approach is to estimate the following multilevel meta-analysis model that captures that arms are nested within experiments, such that true effects of different arms in the same experiment may resemble (e.g. because they were tested in the same election and country):

rma.mv(effect, random = list(~ 1 | experiment/arm), data = df, V = V, mods = ~ voting_prop)

Does the model above look reasonable? Or do we also need to allow true effects to be correlated within voting propensity groups across arms? We think this is not necessary but are not sure. If it is, we are not really sure about which of the approaches below is the best way to specify this. Note voting propensities are not really nested within experiment-arms (effectively this is a cross-classified structure).

rma.mv(effect, random = list(~ 1 | experiment/arm/voting_prop), data = df, V = V, mods = ~ voting_prop)

df\$arm.in.experiment <- paste0(df\$experiment, df\$arm)
rma.mv(effect, random = list(~ 1 | experiment, ~ arm.in.experiment | voting_prop), data = df, V = V, mods = ~ voting_prop)

Any guidance you could provide would be very much welcome.

Francisco Tom�s-Valiente Jord�
ETH Z�rich

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