[R-meta] Guidance for interpreting fixed effects in multilevel models
kj@j@o|omon @end|ng |rom gm@||@com
Wed Aug 11 21:43:08 CEST 2021
Given that you have not provided any output, I can attempt to answer your
question generally. Based on your data description, you likely have an
output similar to what I'm showing further below.
*time0*: Shows the average true effect at time 0 given ktype of 0 and 0
number of treatments (Right off the bat, you may want to center "treats").
Simply change "at time 0" with another value for time, and you'll get the
interpretation of other time values.
*ktype1*: Shows the difference in average true effects between ktype 1 and
ktype 0 at time 0 given 0 number of treatments (again, treats calls for
centering perhaps by its [rounded] mean, or median)
*treats*: Shows how the magnitude of the average true effect is associated
with the number of treatments employed in studies holding time and treats
at their reference values (0 and 0 respectively). Specifically, controlling
these variables, how size of average true effect changes for 1 unit of
increase in treats.
estimate se zval pval
On Sun, Aug 8, 2021 at 10:23 PM Simon Harmel <sim.harmel using gmail.com> wrote:
> A typo correction:
> Model 1: rma.mv(yi ~ 0 + time + ktype + treat, random = list(~ ktype |
> study, ~time | interaction(study, group, outcome), ~1 | esID),struct =
> On Sun, Aug 8, 2021 at 10:12 PM Simon Harmel <sim.harmel using gmail.com> wrote:
> > Dear Colleagues,
> > I want to use two multilevel meta-regression models (below), but was
> > wondering what the correct interpretation of my fixed effects in each one
> > is? (given that the models' random effects are different).
> > In both models, *time* is a factor ranging from 0 to 4; *ktype* is a
> > factor (0=direct,1=indirect) that can vary between studies and between
> > groups in each study (but not between outcomes and time points nested
> > within those groups in each study); and *treat* is a study-level
> > continuous variable (# of treatments in each study).
> > Model 1: rma.mv(yi ~ 0 + time + ktype + treat, random = list(~ ktype |
> > study, ~time | interaction(study, group, outcome), ~1 | esID),struct =
> > c("HAR","HAR"))
> > Model 2: rma.mv(yi ~ 0 + time + ktype + treat, random = list(~ ktype |
> > interaction(study,group,outcome), ~time | study, ~1 | esID), struct =
> > c("UN","UN"))
> > I highly appreciate your expertise and assistance,
> > Simon
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