[R-meta] Sample size and continuity correction

Viechtbauer, Wolfgang (SP) wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Thu Aug 27 15:43:29 CEST 2020

Dear nelly,

See my responses below.

>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org]
>On Behalf Of ne gic
>Sent: Wednesday, 26 August, 2020 10:16
>To: r-sig-meta-analysis using r-project.org
>Subject: [R-meta] Sample size and continuity correction
>Dear List,
>I have general meta-analysis questions that are not
>platform/software related.
>*1. Issue of few included studies *
>* =======================*
>It seems common to see published meta-analyses with few studies e.g. :
>(A). An analysis of only 2 studies.
>(B). In another, subgroup analyses ending up with only one study in one of
>the subgroups.
>Nevertheless, they still end up providing a pooled estimate in their
>respective forest plots.
>So my question is, is there an agreed upon (or rule of thumb, or in your
>view) minimum number of studies below which meta-analysis becomes

Agreed upon? Not that I am aware of. Some may want at least 5 studies (per group or overall), some 10, others may be fine with if one group only contains 1 or 2 studies.

>What interpretations/conclusions can one really draw from such analyses?

That's a vague question, so I can't really answer this in general. Of course, estimates will be imprecise when k is small (overall or within groups).

>*2. Continuity correction *
>* ===================*
>In studies of rare events, zero events tend to occur and it seems common to
>add a small value so that the zero is taken care of somehow.
>If for instance, the inclusion of this small value via continuity
>correction leads to differing results e.g. from non-significant results
>when not using correction, to significant results when using it, what does
>make of that? Can we trust such results?

If this happens, then the p-value is probably fluctuating around 0.05 (or whatever cutoff is used for declaring results as significant). The difference between p=.06 and p=.04 is (very very unlikely) to be significant (Gelman & Stern, 2006). Or, to use the words of Rosnow and Rosenthal (1989): "[...] surely, God loves the .06 nearly as much as the .05". 

Gelman, A., & Stern, H. (2006). The difference between "significant" and "not significant" is not itself statistically significant. American Statistician, 60(4), 328-331.

Rosnow, R.L. & Rosenthal, R. (1989). Statistical procedures and the justification of knowledge in psychological science. American Psychologist, 44, 1276-1284.

>If one instead opts to calculate a risk difference instead, and test that
>for significance, would this be a better solution (more reliable result?)
>to the continuity correction problem above?

If one is worried about the use of 'continuity corrections', then I think the more appropriate reaction is to use 'exact likelihood' methods (such as using (mixed-effects) logistic regression models or beta-binomial models) instead of switching to risk differences (nothing wrong with the latter, but risk differences are really a fudamentally different effect size measure compared to risk/odds ratios).

>Looking forward to hearing your views as diverse as they may be in cases
>where there is no consensus.

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