[R-meta] Two ways to calculate subgroup and overall average effect sizes

[Nathan Pace]

Hi Wolfgang, 

I have a k = 29 SMD meta analysis.

The moderator is a three level factor.

painearly1surgery.rma <- rma(yi = yi, vi = vi, mods = ~ surgery,
                            data = painearly.df, test = 'knha', digits = 3)


painearly2surgery.rma.mv <- rma.mv(yi = yi, V = vi, mods = ~ surgery,
                         random = ~ surgery | study, struct = 'DIAG',
                         data = painearly.df, test = 't', digits = 3)

There is a nearly 10 fold variation in the individual tau^2s.

Variance Components: 

outer factor: study   (nlvls = 29)
inner factor: surgery (nlvls = 3)

           estim   sqrt  k.lvl  fixed  level
tau^2.1    0.082  0.287      8     no   open
tau^2.2    0.759  0.871     10     no    lap
tau^2.3    0.086  0.294     11     no  other


The average tau^2 is:

tau^2 (estimated amount of residual heterogeneity):     0.312 (SE = 0.110)
tau (square root of estimated tau^2 value):             0.559



The omnibus test of moderators  is not rejected in either model.

Test of Moderators (coefficient(s) 2:3):  (average tau^2)
F(df1 = 2, df2 = 26) = 2.082, p-val = 0.145

Test of Moderators (coefficient(s) 2:3):  (individual tau^2)
F(df1 = 2, df2 = 26) = 2.643, p-val = 0.090


Is there a meaningful statistical comparison of the individual tau^2s?



Are there other ways to compare the model fits (AIC or BIC)?

The anova function won’t mix rma.uni and rma.mv objects.


Nathan