[R-meta] Outliers in meta analysis
Michael Dewey
li@t@ @ending from dewey@myzen@co@uk
Thu Oct 11 18:24:51 CEST 2018
Dear Mutlu
I think you first have to decide why you are looking for outliers and
what you intend to do with them when you find them. Only then will it be
clear to you which of the methods you need.
Michael
On 11/10/2018 12:34, Mutlu Umaroglu wrote:
> Dear all,
>
> I am interested in Meta Analysis and studying on outliers. There are
> several method proposed to examine outliers (Viechtbauer 2010).
> According to metafor packages following studies are outlier in this
> example. How can we deside exactly which studies are
> influences/outliers?
>
> Sincerely,
> Mutlu
>
>
> library("metafor")
> data <- dat.bcg
> dat1 <- escalc(measure="RD", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=data)
> res1 <- rma(yi, vi, method="DL",data=dat1)
> influence(res1)
>
> rstudent dffits cook.d cov.r tau2.del QE.del hat weight dfbs
> inf
> 1 -1.4026 -0.0889 0.0079 0.9976 0 273.7960 0.0031 0.3060 -0.0891
> 2 -3.6289 -0.3913 0.1526 0.9563 0 260.2482 0.0067 0.6656 -0.4008
> 3 -1.7694 -0.1874 0.0351 0.9961 0 271.6713 0.0084 0.8424 -0.1885
> 4 -2.5810 -1.9330 2.4532 0.7580 0 167.7548 0.1190 11.9010
> -1.9186 *
> 5 1.2615 0.5217 0.2813 1.1661 0 276.3026 0.1143 11.4253 0.5218
> 6 -9.2559 -1.4557 2.0527 0.6846 0 179.5183 0.0113 1.1260
> -1.7385 *
> 7 -0.8835 -0.2354 0.0551 1.0453 0 271.0926 0.0545 5.4510 -0.2361
> 8 1.6691 1.4621 3.6567 1.8187 0 263.8946 0.1296 12.9579
> 1.5125 *
> 9 1.1381 0.5412 0.3114 1.2042 0 274.9691 0.1216 12.1644 0.5424
> 10 -3.4172 -0.9551 0.8758 0.9605 0 248.2341 0.0520 5.2024
> -0.9795 *
> 11 1.3748 1.1033 1.7292 1.5523 0 274.9184 0.1287 12.8677
> 1.1272 *
> 12 1.8398 0.7986 0.6771 1.2027 0 274.4115 0.1217 12.1716
> 0.8004 *
> 13 1.6603 1.3631 2.9509 1.7076 0 270.6094 0.1292 12.9187
> 1.4029 *
>
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--
Michael
http://www.dewey.myzen.co.uk/home.html
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