[R-meta] comparing rma to lm
Tom, I would offer two potential explanations for why your results differ in using rma as oppose to lm---one good and the other potentially problematic. The good possibility is that meta-analytic models might be giving you improved precision. Meta-regression is just weighted least squares regression, where the weights are chosen to optimize the use of information from larger and smaller studies/samples. If the studies in your analysis vary widely in size/precision, then maybe meta-regression is making more efficient use of the data, and thus leading to smaller SEs (and more statistically significant results). To see whether this is the case: compare the SEs from rma to the SEs from lm (I would suggest using robust SEs from the sandwich package for the latter). The bad possibility is that the default methods for calculating hypothesis tests and confidence intervals in rma are based on large-sample approximations, whereas the defaults with lm use methods (t-tests rather than z-tests) that are more accurate when the number of studies is small. If this is what makes the difference, then the extra-significant results from rma could be spurious. Using the Knapp-Hartung correction (test = "knha") will improve the small-sample calibration of the meta-analysis tests. You could try turning that on to see if it makes a difference. Hook 'em, James On Wed, Sep 12, 2018 at 6:33 PM Juenger, Thomas E <
tjuenger at austin.utexas.edu> wrote:
Hi:
I study plant quantitative genetics. We use statistical analyses to map
genes controlling plant traits. I'm emailing to ask a few simple questions
about meta-analyses using metafor in R.
My research program centers on how the effect of inheriting alternative
alleles at a genetic locus is altered by environmental variation. We call
this gene-by-environment interaction (GxE). We can study this phenomenon
by growing our mapping populations in different environmental contexts -
this can be different greenhouses, growth chambers, treatment applications
or field sites in nature. Ultimately we end up with an effect estimate
(the mean difference between individuals carrying alternative alleles - we
call this the "additive effect" in quantitative genetics) and standard
error for each locus affecting a trait in each environmental context. Our
mapping approach often involves mixed models to test how the effect of
alleles changes by condition. However, we generally do not know the
mechanistic driver or cause of the GxE and we imagine it can differ among
the many loci influencing a particular trait of interest.
Our most recent experiment grows a mapping population at 10 different
field locations. We'd like to look for drivers of the GxE using a
regression approach. We started running simply lm models to ask how
various climate factors affected the additive effect across the 10
experiments - things like latitude, temperature, rainfall. A friend
mentioned that it could be interesting to think about this as a
meta-analysis problem, and include our error estimates when looking for
covariates/moderators that drive the GxE. The suggestion seems a good one
given we have excellent data about the uncertainty of the effects. We've
just started looking at some basic analyses using rma.
My initial thought is that we would see less significant results when
taking into account the uncertainty in the additive effect estimates.
However, we actually see the opposite. In every case our rma models have
more significant covariates/moderators than simple linear models. I'm
surprised by this and am trying to understand why this might be so.
Any thoughts or ideas - I have a feeling I'm missing something simple...
Tom
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