[R-meta] Parameter redundancy

Gerta Ruecker Ruecker @end|ng |rom |mb|@un|-|re|burg@de
Sat Jun 15 22:40:30 CEST 2019


Dear all,

Further references:

(Odds ratio, to understand the problem:) 
Schwarzer G, Antes G, Schumacher M. Inflation of type I error rate in two statistical tests for the detection of publication bias in meta-analyses with binary outcomes. Stat Med. 2002 Sep 15;21(17):2465-77.

(Arcsine test:)
Rücker G, Schwarzer G, Carpenter J. Arcsine test for publication bias in meta-analyses with binary outcomes. Stat Med. 2008 Feb 28;27(5):746-63.

Rücker G1, Carpenter JR, Schwarzer G. Detecting and adjusting for small-study effects in meta-analysis. Biom J. 2011 Mar;53(2):351-68. doi: 10.1002/bimj.201000151. Epub 2011 Jan 14.

(General recommendations:)
Sterne JA et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011 Jul 22;343:d4002. doi: 10.1136/bmj.d4002.


----------------ursprüngliche Nachricht-----------------
Von: James Pustejovsky [jepusto using gmail.com]
An: "Viechtbauer, Wolfgang (SP)" [wolfgang.viechtbauer using maastrichtuniversity.nl]
Kopie: Magnus Magnusson [Magnus.Magnusson using slu.se], r-sig-meta-analysis using r-project.org
Datum: Sat, 15 Jun 2019 14:39:24 -0500
> Magnus,
> Following up on Wolfgang's reply, here are some pointers to methodological
> articles on how this problem plays out (and how to fix it!) with different
> effect size metrics:
> - Odds ratios: Moreno SG, Sutton AJ, Ades A, et al. Assessment of
>    regression-based methods to adjust for publication bias through
> a comprehensive simulation study. BMC Med Res Methodol. 2009;9(1):17.
>    https://doi.org/10.1186/1471-2288-9-2
> - Raw proportions: Hunter JP, Saratzis A, Sutton AJ, Boucher RH, Sayers
> RD, Bown MJ. In meta-analyses of proportion studies, funnel plots were
> found to be an inaccurate method of assessing publication bias. J Clin
>    Epidemiol. 2014;67(8):897-903.
>    https://doi.org/10.1016/j.jclinepi.2014.03.003
> - Hazard ratios: Debray TP, Moons KG, Riley RD. Detecting small-study
> effects and funnel plot asymmetry in meta-analysis of survival data: a
> comparison of new and existing tests. Res Synth Methods. 2018;9(1):41-50.
>    https://doi.org/10.1002/jrsm.1266
> - Standardized mean differences: Pustejovsky JE, Rodgers MA. Testing for
> funnel plot asymmetry of standardized mean differences. Res SynMeth.
>    2019;1-15 https://doi.org/10.1002/jrsm.1332
> James
> On Sat, Jun 15, 2019 at 1:36 PM Viechtbauer, Wolfgang (SP) <
> wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>> Hi Magnus,
>> My point was that for certain outcome/effect-size measures, the sampling
>> variance is a function of the size of the outcome/effect. For example:
>> - for the raw correlation coefficient, the usual large-sample
>> approximation to the sampling variance is (1-r^2)^2 / (n-1), which depends
>> on r
>> - for the standardized mean difference, the usual large-sample
>> approximation to the sampling variance is 1/n1 + 1/n2 + d^2 / (2*(n1+n2)),
>> which depends on d
>> For other measures, there can also be such dependencies, although
>> sometimes they are not as obvious.
>> Hence, if we use a form of the 'regression test' (to check for funnel plot
>> asymmetry) where we use the sampling variance (or some function thereof,
>> such as its square root) as the 'predictor', then this can result in
>> inflated Type I error rates of the regression test. To avoid this problem,
>> we can use the sample size (or some function thereof, such as its
>> reciprocal) as the predictor or use an outcome measure where the sampling
>> variance is not a function of the size of the outcome/effect (e.g., those
>> that are obtained via a variance-stabilizing transformation, such as the
>> r-to-z transformed correlation coefficient or the arcsine square root
>> transformed risk difference).
>> Best,
>> Wolfgang
>> -----Original Message-----
>> From: R-sig-meta-analysis [mailto:
>> r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Magnus Magnusson
>> Sent: Saturday, 15 June, 2019 20:19
>> To: r-sig-meta-analysis using r-project.org
>> Subject: [R-meta] Parameter redundancy
>> Dear all,
>> I am using the metafor package (rma.mv) and is currently evaluating
>> publication bias for a multilevel model by using the Eggers regression test.
>> I saw in a post answered by the package author, Wolfgang Viechtbauer, at
>> the cross validated forum that for some measures you have to be aware of
>> potential parameter redundancy (between the measure and the variance of the
>> measure) when using the test.
>> I wonder (1) which measures this refers to and (2) how severe this problem
>> likely is for the judging the outcome of a pub-bias test.
>> Best wishes,
>> Magnus Magnusson, postdoc at the Swedish University of Agricultural
>> Sciences based in Umeå
>> --------------------------------------------------------------------
>> Magnus Magnusson
>> Post doc position at
>> Department of Wildlife, Fish and Environmental Studies
>> Swedish University of Agricultural Sciences
>> SE-901 83 Umeå
>> Sweden
>> phone: +46(0)90-7868587
>> e-post: magnus.magnusson using slu.se
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Gerta Ruecker
Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center - University of Freiburg
Postal Address: Stefan-Meier-Str. 26, 79104 Freiburg
Phone: +49/761/	203-6673
Mail: Ruecker using imbi.uni-freiburg.de 
Homepage: http://www.imbi.uni-freiburg.de

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