[R-meta] Benefits to metafor when missing vi estimates?

[Bronwen Stanford]

I am conducting a meta-analysis on a dataset that contains sample size and
error estimates for only 15% of the data points. I'm constructing a
mixed-effects (multi-level) model using rma.mv, and the model includes one
random effect (representing study) and multiple fixed effects, both
continuous and categorical. I have been advised to use metafor and assign a
constant value to vi (e.g. vi=1) for all data points without error
estimates to improve the model estimates of standard errors.  However,
based on answers such as

https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2017-October/000252.html

this seems like potentially an inappropriate use of metafor - I'm telling
the model I have information about variance when variance is in fact
unknown (and my dataset does not qualify for a "true" meta-analysis).

My coefficient estimates using metafor (with vi=1) and lmer (or lme) are
also different (in both magnitude and significance), which concerns me. Any
thoughts on the most appropriate way to approach this less-than-ideal
dataset? Does using metafor in this case (with a constant vi value) improve
model accuracy, or is it reasonable to stick with standard mixed-effects
modeling packages?

Thanks!

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