[R-meta] RVE or not RVE in meta-regressions with small number of studies?
Röhl, Sebastian
@eb@@t|@n@roeh| @end|ng |rom un|-tueb|ngen@de
Sun Apr 23 19:25:38 CEST 2023
Dear James and Wolfgang,
thank you so much for your detailed and helpful answers that make some things clearer for me and are very helpful for my further research.
In my experience, the CRVE SEs are significantly larger than SEs from a pure HE model, which I have seen in many meta-analyses in recent years. Of course, I agree with you that this is actually a sign of model misspecification and can actually lead to erroneous conclusions. On the other hand, it has been my experience several times that reviewers do not understand the use of CRVE and more complex models and are then bothered by non-significant moderator effects ("The study does not provide any new insight and should therefore be rejected").
However, this seems to be a general problem in science (keyword: publication bias)... It is especially painful for people who are rather at the beginning of their career and urgently need the publications. With older colleagues, I've noticed much more often in conversations the "wisdom" that we often have few validated findings in research when we apply methodological rigor.
So, in the end, we can only hope that scientific-ethical behavior prevails over fast career thinking.
Enough complaining - thanks again for your responses and the opportunity to share on this list!
Best,
Sebastian
Von: James Pustejovsky <jepusto using gmail.com>
Gesendet: Donnerstag, 20. April 2023 19:51
An: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis using r-project.org>
Cc: Röhl, Sebastian <sebastian.roehl using uni-tuebingen.de>
Betreff: Re: [R-meta] RVE or not RVE in meta-regressions with small number of studies?
Wolfgang, thanks for jumping in (have been swamped so not much time for mailing list correspondence).
Surprising nobody, my perspective is very much in agreement with the argument Wolfgang laid out. I think it's useful to think about these questions in three stages:
1. What working model should you use? The cited paper used robumeta so either a CE or HE working model. As Sebastian points out, the CHE working model is more flexible and lets you decompose heterogeneity across levels of the model. Generally, the best working model is the one that most closely approximates the real data-generating process.
2. How should you calculate standard errors? From the analyst's point of view, the more work you put in on checking the working model specification, the better position you will be in to trust its assumptions. If you are willing to accept the assumptions (including homoskedasticity of random effects at each level, etc.), then it is reasonable to use the model-based standard errors generated by rma.mv<http://rma.mv>(). On the other hand, if there are substantial differences between the model-based SEs and the cluster-robust SEs, that is probably a sign that the working model is mis-specified in some way, which casts doubt on trusting the model-based SEs.
3. How should you do inference (hypothesis testing and confidence intervals)? Here, there is a further difference between model-based approaches and cluster-robust approaches. The model-based approaches (either Wald tests with model-based standard errors or likelihood ratio tests) involve asymptotic approximations, so you need to gauge whether your database includes a large enough number of studies to trust the asymptotic approximation. (In principle, one could use small-sample adjustments to Wald tests, such as the Kenward-Roger corrections, but these are not implemented in metafor). Robust variance estimation as implemented in robumeta or clubSandwich uses methods with small-sample adjustments (such as Satterthwaite degrees of freedom) that perform well even in quite small samples. Thus, another reason there might be differences between model-based CIs and robust CIs is that the robust CIs are based on more accurate approximations, so apparent advantages of the model-based CI might be illusory.
Further inline comments below.
James
On Tue, Apr 18, 2023 at 9:13 AM Röhl, Sebastian via R-sig-meta-analysis <r-sig-meta-analysis using r-project.org<mailto:r-sig-meta-analysis using r-project.org>> wrote:
Dear all,
I came across an article in RER that argues that one could or should forgo RVE for analysis of categorical moderators in case of smaller study numbers:
Cao, Y., Grace Kim, Y.‑S., & Cho, M. (2022). Are Observed Classroom Practices Related to Student Language/Literacy Achievement? Review of Educational Research, 003465432211306. https://doi.org/10.3102/00346543221130687
Page 10: “We acknowledge the superiority of robust variance estimation (RVE) for handling dependent effect sizes. However, it has a few important limitations. First, it neither
models heterogeneity at multiple levels nor provides corresponding hypothesis tests.
When the authors refer to "RVE" here, I think they are referencing the models implemented in the robumeta package. These models (the CE and HE working models) are indeed limited in terms of modeling heterogeneity at multiple levels and limited in that they do not provide means of conducting hypothesis tests about variance components. As Sebastian noted, the first limitation can be resolved by using the CHE or related working models. The second limitation can be resolved in some sense by using ML or REML estimation of variance components. One can then use likelihood ratio tests for the variance components, although such tests are not "robust" in the sense of RVE. Rather, they are predicated (at least to some extent?) on having correctly specified the working model.
Second, the power of the categorical moderator highly depends on the number of studies and features of the covariate (Tanner-Smith, Tipton, & Polanin, 2016). When the number of studies is small, the test statistics and confidence intervals based on RVE can have inflated Type I error (Hedges et al., 2010; Tipton & Pustejovsky, 2015).
Inflated Type I error is true for RVE not involving small-sample corrections (i.e., the approaches called CR0 or CR1 in clubSandwich, or the approach implemented in metafor::robust() with clubSandwich = FALSE). Inflated Type I error is much less of an issue with the CR2 adjustment and Satterthwaite degrees of freedom.
Relating to our cases, many of our moderators had imbalanced distributions […]. Consequently, tests of particular moderators may be severely underpowered.”
Of course, the first argument can be invalidated by the use of correlated hierarchical effects models with RVE. However, I find the second argument very relevant from my experience.
As Wolfgang noted, the question here is: "severely underpowered" relative to what alternative?
In the social sciences, after all, we more often conduct meta-analyses with relatively small study corpus (n<100 or n<50). In high-ranked journals in this research field (e.g., Psychological Bulletin, Review of Educational Research, Educational Research Review…) I very rarely find the use of RVE / CRVE.
I think this is changing (finally). Recent submissions to Psych Bulletin regularly use RVE/CRVE, but RER and ERR have been slower to shift practice.
In mentioned types of moderator analyses with small number of studies in one category, I also often face the same problem that effects become non-significant when using CRVE as soon as moderator levels are populated with less than 10-15 studies. Joshi et al (2022) also talk about RVE being (too) highly conversative in these cases.
Joshi's comments about tests being too conservative here pertain to hypothesis tests involving multiple contrasts, such as testing the equality of effect sizes across a moderator with 3 or 4 categories (mu_1 = mu_2 = mu_3, etc.). For single-parameter tests and confidence intervals, CR2 standard errors and Satterthwaite degrees of freedom are well calibrated unless the degrees of freedom are very small (df < 4, as suggested in Tipton, 2015).
I have also used cluster wild bootstrapping for significance testing of individual effects in this case. However, the problem of missing SEs and C.I.s as well as the high computation time arises here.
Have you tried the latest version of wildmeta? From version 0.3.1 (released in February), parallel processing is supported, which can help with computation time quite a bit. But again, this is really only relevant for hypothesis tests involving multiple contrasts.
Right now, I am again facing the problem of model selection for a meta-analysis with about 50 studies and 500 ES (correlations). Since we are dealing with ES within studies, I would choose a correlated hierarchical effects model with CRVE, which also works very well for the main effects, but again leads to said very large SEs for the moderators. As a pure CHE model (which in my opinion still fits better than the pure HE model in the above mentioned article by Cao et al) the SEs are of course somewhat more moderate.
Do you have any tips or hints for an alternative?
Two things to consider:
A. Have you tried group-mean centering the predictors? It could be a contextual effects issue that leads to discrepancies between model-based and robust SEs.
B. If that doesn't resolve the issue, then it seems like the discrepancy could be driven by mis-specification of the working model (see my point #2 above). If you group-mean center the predictors, you could include random slopes in the model to see if there is heterogeneity in the within-study slopes. Unmodeled random slopes could again lead to discrepancies between model-based and robust SEs.
****************************
Dr. Sebastian Röhl
Eberhard Karls Universität Tübingen
Institut für Erziehungswissenschaft
Tübingen School of Education (TüSE)
Wilhelmstraße 31 / Raum 302
72074 Tübingen
Telefon: +49 7071 29-75527
Fax: +49 7071 29-35309
E-Mail: sebastian.roehl using uni-tuebingen.de<mailto:sebastian.roehl using uni-tuebingen.de><mailto:sebastian.roehl using uni-tuebingen.de<mailto:sebastian.roehl using uni-tuebingen.de>>
Twitter: @sebastian_roehl @ResTeacherEdu
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