[R-meta] Wald_test - is it powerful enough?

[Cátia Ferreira De Oliveira]

Hello,

I hope you are well.
Is the Wald_test a lot less powerful than the QM test? I ask this because
in the example below the QM test is significant but the Wald test is not,
shouldn't they be equivalent?
If it is indeed the case that the Wald_test is not powerful enough to
detect a difference, is there a good equivalent test more powerful than the
Wald test that can be used alongside the robumeta package?

Best wishes,

Catia

*dat <- dat.assink2016*



*V <- impute_covariance_matrix(dat$vi, cluster=dat$study, r=0.6)*



*# fit multivariate model with delinquency type as moderator*

*res <- rma.mv <http://rma.mv>(yi, V, mods = ~ deltype-1, random = ~
factor(esid) | study, data=dat)*

*res*



*Multivariate Meta-Analysis Model (k = 100; method: REML)*



*Variance Components:*



*outer factor: study        (nlvls = 17)*

*inner factor: factor(esid) (nlvls = 22)*



*estim    sqrt  fixed*

*tau^2      0.2150  0.4637     no*

*rho        0.3990             no*



*Test for Residual Heterogeneity:*

*QE(df = 97) = 639.0911, p-val < .0001*



*Test of Moderators (coefficients 1:3):*

*QM(df = 3) = 28.0468, p-val < .0001*



*Model Results:*



*estimate      se     zval    pval    ci.lb <http://ci.lb>   ci.ub*

*deltypecovert    -0.2902  0.2083  -1.3932  0.1635  -0.6984  0.1180*

*deltypegeneral    0.4160  0.0975   4.2688  <.0001   0.2250  0.6070  ****

*deltypeovert      0.1599  0.1605   0.9963  0.3191  -0.1546  0.4743*



*---*

*Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1*



*Wald_test(res, constraints=constrain_zero(1:3), vcov="CR2",
cluster=dat$study)*



*test Fstat df_num df_denom  p_val sig*

*  HTZ  40.9      3     1.08 0.0998   .*



Thank you,


Catia

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