[R-meta] Ratio of Means
Viechtbauer Wolfgang (SP)
wolfgang.viechtbauer at maastrichtuniversity.nl
Wed Jul 19 10:30:47 CEST 2017
I would say the canonical reference on the log transformed ratio of means (often called the 'response ratio' in the field of ecology) is:
Hedges, L. V., Gurevitch, J., & Curtis, P. S. (1999). The meta-analysis of response ratios in experimental ecology. Ecology, 80(4), 1150-1156.
If you are using metafor, the data used in that paper can be found under dat.curtis1998 (i.e., help(dat.curtis1998) for more details).
This measure is used quite a lot in meta-analyses in that field since the measurements in those studies tend to be ratio scale measurements (e.g., the weight/mass/height of plants grown under different experimental conditions). And in fact, that is the big caveat: This outcome measure is only applicable for ratio scale measurements. Note that the examples in Friedrich, Adhikari, & Beyene (2008) are also all based on things that could be considered ratio scale measurements (urine output, serum creatinine, and creatinine clearance).
So, as long as you are dealing with such measurements, I think the response ratio is a perfectly fine outcome measure for a meta-analysis.
Minor sidenote: In rma.uni(), you should use test="knha" instead of knha=TRUE. The latter still works, but is now undocumented.
Wolfgang Viechtbauer, Ph.D., Statistician | Department of Psychiatry and
Neuropsychology | Maastricht University | P.O. Box 616 (VIJV1) | 6200 MD
Maastricht, The Netherlands | +31 (43) 388-4170 | http://www.wvbauer.com
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Nathan Pace
Sent: Wednesday, July 19, 2017 07:19
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Ratio of Means
In two papers (J Clin Epidemiol 2011;64:556–564. BMC Med Res Method 2008;8(32)DOI: 10.1186/1471-2288-8-32)
Friedrich, Adhikari, and Beyene proposed methods for the meta analysis of the Ratio of Means of continuous outcomes.
Using the reported means, standard deviations, and sample sizes of the experimental and control groups, the log of the ratio of means and the associated standard error using a first order delta methods are estimated. This meta analysis then uses the generic inverse variance method.
I have a k = 23 meta analysis. I used the metafor function rma.uni with REML and knha = T; a moderator was included.
Any unexpected pitfalls?
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