[R-meta] Outlier and influence cases 3-level meta-analysis
Dear Daniel, No fudamental issues, except that I have not yet implemented all of this for 'rma.mv' objects. Assuming that you have installed the 'devel' version of metafor (https://wviechtb.github.io/metafor/#installation), things like rstandard(), rstudent(), cooks.distance(), and dfbetas() are however already available to you, which are some of the main tools for detecting outliers and/or influential cases anyway (covariance ratios would also be nice, but not yet available). There is also hatvalues() and weights(), but these may be a bit less accessible. One issue in more complex models is the question at which 'level' we want to detect outliers / influential cases. For rma.mv objects, things like rstandard() and cooks.distance() have a 'cluster' argument, which can be used to specify a grouping variable. For example: dat <- dat.konstantopoulos2011 res <- rma.mv(yi, vi, random = ~ 1 | district/school, data=dat) cooks.distance(res) will compute the Cook's distances for each individual estimate, while cooks.distance(res, cluster=dat$district) will compute the Cook's distances for each level of 'district'. Or easier for interpretation: plot(cooks.distance(res), type="o", pch=19) suggests maybe 2 somewhat influential estimates and plot(cooks.distance(res, cluster=dat$district), type="o", pch=19) one influential district (probably the one with those 2 estimates, but I haven't checked). Best, Wolfgang
-----Original Message----- From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of daniel.f.gucciardi at gmail.com Sent: Wednesday, 17 March, 2021 9:44 To: r-sig-meta-analysis at r-project.org Subject: [R-meta] Outlier and influence cases 3-level meta-analysis Hi all, My colleagues and I have conducted a 3-level meta-analysis using metafor in which we account for multiple outcomes from individual studies. As part of our sensitivity analyses, we examined outlier and influential cases using the recommendations of Viechtbauer and Cheung (https://onlinelibrary.wiley.com/doi/10.1002/jrsm.11), which seems to be a common approach. Are there any issues in applying their framework for two-level meta-analytic models to three-level models? If so, I'd appreciate your direction to any literature that might help me appreciate these issues please. Regards, Daniel