[R-meta] Response Ratios in nested studies

[James Pustejovsky]

Yes, that's right.

On Tue, Oct 19, 2021 at 10:50 AM Farzad Keyhan <f.keyhaniha using gmail.com>
wrote:

> Oh, I may have misunderstood the correct application of DEF. So, this
> is a study-specific formula, right? That is, "n-lower" is the average
> cluster size in each study not across all studies?
>
> DEF = (n-lower - 1) * ICC + 1
>
> Fred
>
> On Tue, Oct 19, 2021 at 10:35 AM James Pustejovsky <jepusto using gmail.com>
> wrote:
> >
> > Hi Fred,
> >
> > Do you have information about cluster sizes among the studies with
> nested structure? If you do, and if their average cluster sizes differ,
> then it would be better to use this information to calculate a unique DEF
> for each study. Even if you assume a common ICC, the DEF will not
> necessarily be identical for every study.
> >
> > James
> >
> > On Tue, Oct 19, 2021 at 10:28 AM Farzad Keyhan <f.keyhaniha using gmail.com>
> wrote:
> >>
> >> Absolutely, so I will proceed with the vi given by metafor::escalc()
> >> and then multiply the vi given by escalc() by a common DEF with a
> >> common ICC across the studies that have nested structure and then
> >> change that common ICC and inspect the change in coefficients (I'll
> >> probably do this on a null model without moderators).
> >>
> >> Once again, many thanks,
> >> Fred
> >>
> >> On Tue, Oct 19, 2021 at 10:15 AM James Pustejovsky <jepusto using gmail.com>
> wrote:
> >> >
> >> > Hi Fred,
> >> >
> >> > Yes, it is definitely possible and sensible to combine the DEF
> correction with RVE meta-analysis. However, I think it may be important to
> use the initial DEF correction (and accompanying sensitivity analysis),
> even if it is only based on ballpark assumptions. Without it, studies with
> clustered samples will get an inordinately large amount of weight in the
> meta-analysis, leading to imprecise estimates of average effects and
> inflated estimates of between-study heterogeneity.
> >> >
> >> > James
> >> >
> >> > On Tue, Oct 19, 2021 at 10:07 AM Farzad Keyhan <f.keyhaniha using gmail.com>
> wrote:
> >> >>
> >> >> Dear Reza and James,
> >> >>
> >> >> Thank you so much for your, as always, valuable advice. Can we
> >> >> possibly combine your two suggestions?
> >> >>
> >> >> I mean can we both correct the initial, incorrect sampling variances
> >> >> and then apply the clubSandwich package?
> >> >>
> >> >> The reason is that finding the correct ICC is one issue, but then
> >> >> assuming that ICC is going to be the same across the groups is
> another
> >> >> issue which together make such a correction possibly a bit imprecise.
> >> >>
> >> >> Thanks much,
> >> >> Fred
> >> >>
> >> >>
> >> >> On Tue, Oct 19, 2021 at 9:30 AM James Pustejovsky <jepusto using gmail.com>
> wrote:
> >> >> >
> >> >> > Hi Fred,
> >> >> >
> >> >> > This is a good question. I am in the same boat as Reza, as I don't
> know of any methods work that examines the issue (though it seems like the
> sort of thing that must be out there?). I'm going to respond under the
> assumption that you don't have access to raw data and are just working with
> reported summary statistics from a set of studies, some or all of which
> ignored the clustering issue.
> >> >> >
> >> >> > My first thought would be to use the same sort of
> cluster-correction that is used for raw or standardized mean differences.
> The variance of the LRR is based on a delta method approximation, and it
> can be expressed as
> >> >> >
> >> >> > vi = se1^2 / m1^2 + se2^2 / m2^2,
> >> >> >
> >> >> > where se1 = sd1 / sqrt(n1) and se2 = sd2 / sqrt(n2) are the
> standard errors of the means in each group (calculated ignoring clustering,
> assuming a sample of independent observations). The issue with clustered
> data is that the usual standard errors are too small because of dependent
> observations. The usual way to correct the issue is to inflate the standard
> errors by the square root of the design effect, defined as
> >> >> >
> >> >> > DEF = (n-lower - 1) * ICC + 1,
> >> >> >
> >> >> > where n-lower is the number of lower-level observations per
> cluster (or the average number of observations per cluster, if there is
> variation in cluster size) and ICC is an intra-class correlation describing
> the proportion of the total variation in the outcome that is between
> clusters.
> >> >> >
> >> >> > If we assume that the ICC is the same in each group, then the
> design effect hits both standard errors the same way, and so we can just use
> >> >> >
> >> >> > vi = DEF * (se1^2 / m1^2 + se2^2 / m2^2),
> >> >> >
> >> >> > In some areas of application, it can be hard to find empirical
> information about ICCs, in which case you may just have to make some rough
> assumptions in calculating the DEF then conduct sensitivity analysis for
> varying values of ICC.
> >> >> >
> >> >> > If my initial assumption is wrong and you do have access to raw
> data, then the following recent article might be of help:
> >> >> > https://doi.org/10.1002/sim.9226
> >> >> >
> >> >> > Best,
> >> >> > James
> >> >> >
> >> >> > On Fri, Oct 15, 2021 at 9:00 PM Farzad Keyhan <
> f.keyhaniha using gmail.com> wrote:
> >> >> >>
> >> >> >> Hello All,
> >> >> >>
> >> >> >> I recently came across a post
> >> >> >> (
> https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2021-October/003330.html
> )
> >> >> >> that discussed an issue that is relevant to my meta-analysis.
> >> >> >>
> >> >> >> In short, if some studies have nested structures, and the effect
> size
> >> >> >> of interest is log response ratio (LRR), is there a way to adjust
> the
> >> >> >> sampling variances (below) before modeling the effect sizes?
> >> >> >>
> >> >> >> vi = sd1i^2/(n1i*m1i^2) + sd2i^2/(n2i*m2i^2)
> >> >> >>
> >> >> >> Thank you,
> >> >> >> Fred
> >> >> >>
> >> >> >> _______________________________________________
> >> >> >> R-sig-meta-analysis mailing list
> >> >> >> R-sig-meta-analysis using r-project.org
> >> >> >> https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis
>

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