[R-meta] To include or not include time in rma.mv

[James Pustejovsky]

Hi Yuhang,

As one of the authors of the second study you mentioned, I think you may be
misinterpreting our analysis. My understanding is that the data that we
used to develop our examples did not include standardized mean differences
prior to treatment. All of the included effect sizes were for
post-treatment effects (i.e., `postwks >= 0`).

I can't say for certain, but I suspect that the same thing holds for the
Williams et al. meta-analysis (the first study you mentioned). If you look
at their Table 2, the "Outcome timing" variable ranges from "midstream
during intervention" to "follow-up post-test," so there don't appear to be
any pre-treatment effect sizes included in the analysis.

If the data did include pre-test effect sizes, then I fully agree that m1
would blur the interpretation of the treatment effects.

James

On Mon, Sep 5, 2022 at 7:36 PM Yuhang Hu <yh342 using nau.edu> wrote:

> Hello All,
>
> I'm considering two candidate fixed-effects structures (cat_mod
> = categorical moderator) to meta-analyze a group of longitudinal
> studies (pre-test and follow-ups):
>
> m1 = rma.mv(yi ~ 0 + cat_mod + covariates, random = ~ 1 | study/effect)
> m2 = rma.mv(yi ~ 0 + cat_mod*time + covariates, random = ~ 1 |
> study/effect)
>
> In several authoritative meta-analyses of longitudinal studies such as
> ***https://doi.org/10.1080/19345747.2021.2009072*** and
> ***https://doi.org/10.1007/s11121-021-01246-3***, I noticed the authors
> used "m1" over "m2".
>
> But I was wondering, wouldn't, ignoring "time" (as in "m1") in
> the fixed-effects structure mix up the pre-test and follow-up effect
> sizes together which can blur our understanding of the treatment effect?
>
> Your guidance is highly appreciated,
>
> Yuhang
>
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