# [R-meta] Redundant predictors

Viechtbauer, Wolfgang (SP) wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Sun Jun 28 17:37:34 CEST 2020

Dear Arne,

Your understanding of the fail-safe N is correct, although the way this number is often computed makes use of Stouffer's method for pooling the p-values of the studies and doesn't actually make use the effect sizes. To illustrate:

library(metafor)

yi <- c(0.1, 0.07, 0.26, 0.24, 0.19, -0.02, 0.09, -0.04, 0.18, -0.08, -0.18, 0.3, -0.09, 0.06, 0.15, -0.05)
vi <- c(0.00943, 0.00134, 0.01923, 0.00962, 0.01449, 0.01613, 0.00585, 0.0031, 0.01818, 0.0013, 0.01887, 0.01136, 0.00885, 0.00187, 0.00645, 0.01613)

rma(yi, vi, method="FE")

So a meta-analysis (using a FE model) yields a significant effect. Now let's compute the fail-safe N:

fsn(yi, vi)

This says that 35 studies with a null result would yield a non-significant effect. This approach uses Stouffer's method for pooling the (one-sided) p-values. The p-value for the 16 studies using this method is obtained with:

zi <- yi / sqrt(vi)
pnorm(sum(zi) / sqrt(16), lower.tail=FALSE)

So, if we would add 35 studies that have (on average) a z-statistic of 0, then we would get a non-significant pooled p-value:

pnorm(sum(zi) / sqrt(16 + 35), lower.tail=FALSE)

So that checks out. But note that this makes no reference to effects - it just uses the p-values and hence z-statistics of the studies.

An approach that is based on the same idea as a FE model is the one by Rosenberg:

fsn(yi, vi, type="Rosenberg")

This says that 3 studies with null effects would need to be added to render the FE model non-significant (where the sampling variances of those 3 effects are assumed to be equal to the harmonic mean of the sampling variances of the observed effects). So in other words:

yi.fsn <- c(yi, rep(max(yi), 3))
vi.fsn <- c(vi, rep(1/mean(1/vi), 3))
rma(yi.fsn, vi.fsn, method="FE")

And indeed, that just fails to be significant at alpha = .05.

Using the same idea, we could reverse this process. Let's say we start with these effects, which yield a non-significant result based on a FE model (p = .29):

yi <- c(0.05, 0.07, 0.10, 0.14, 0.02, -0.15, 0.09, -0.04, 0.11, -0.08, -0.18, 0.22, -0.09, 0.06, 0.11, -0.05)
rma(yi, vi, method="FE")

Now by trial-and-error, I can easily figure out that 2 studies with an effect equal to the maximum observed effect are needed to make the FE model significant:

yi.fsn <- c(yi, rep(max(yi), 2))
vi.fsn <- c(vi, rep(1/mean(1/vi), 2))
rma(yi.fsn, vi.fsn, method="FE")

If one would want to do this in the context of a RE model, things get more tricky because one would have to factor in the between-study variance component. So, we start with:

res <- rma(yi, vi)
res

Now we could assume that the new studies being added come from the same population of studies and their addition does not alter the estimate of tau^2. Then we again just need to add 2 studies here:

yi.fsn <- c(yi, rep(max(yi), 2))
vi.fsn <- c(vi, rep(1/mean(1/vi), 2))
rma(yi.fsn, vi.fsn, tau2=res\$tau2)

Note that I fix tau^2 to the value obtained from the RE model fitted to the observed data based on my earlier assumption.

But adding studies with such large effects does actually drive up the heterogeneity, so if one were to reestimate tau^2, the result would not be significant:

rma(yi.fsn, vi.fsn)

If we do that, then we would need to add 3 studies:

yi.fsn <- c(yi, rep(max(yi), 3))
vi.fsn <- c(vi, rep(1/mean(1/vi), 3))
rma(yi.fsn, vi.fsn)

In case you try something like this with your own data, I would be interested in hearing what you find.

Best,
Wolfgang

>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org]
>On Behalf Of Arne Janssen
>Sent: Sunday, 28 June, 2020 16:31
>To: 'r-sig-meta-analysis using r-project.org'
>Subject: [R-meta] Redundant predictors
>
>L.S.,
>
>As far as I understand, the fail-safe N analysis serves to estimate the
>number of cases with zero effect size that would have to be added to
>turn a significant effect size just not significant anymore. Is there
>also an opposite test, i.e. how many cases with significant effect (for
>example the case with the most extreme effect size in the dataset) that
>would have to be added to turn a non-significant effect size into a
>significant one?
>
>Best wishes,
>Arne Janssen