[R-meta] Rationale for performing a moderator test without heterogeneity
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On 05/03/2022 15:34, racasuso wrote:
Dear all, I am performing a meta-analysis on the effects of muscle disuse on muscle loss. We choose age, duration of the intervention and initial muscle strength as a priory moderators. The meta-analysis for muscle loss is as follows: Random-Effects Model (k = 30; tau^2 estimator: DL) ? logLik? deviance?????? AIC?????? BIC????? AICc -17.9973?? 27.8366?? 39.9946?? 42.7970?? 40.4391 ? tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0511) tau (square root of estimated tau^2 value):????? 0 I^2 (total heterogeneity / total variability):?? 0.00% H^2 (total variability / sampling variability):? 1.00 Test for Heterogeneity: Q(df = 29) = 27.8366, p-val = 0.5267 Model Results: estimate????? se???? zval??? pval??? ci.lb??? ci.ub -0.3986? 0.0803? -4.9619? <.0001? -0.5561? -0.2412? *** My first question is if there is any rationale to further perform the moderator test. In fact, when I perform it for initial force the test of moderators is significant. How can I interpret this?
If you had decided a priori to test those moderators then you would usually do that irrespective of observed heterogeneity and report the results. It can happen that the amount of heterogeneity is not sufficient for the Q value to exceed some level of statistical significance but there is still enough for the moderators to explain some of it.
Second, I am a little bit confused on how to interpret the test for moderators when I perform it for each variable in separate and when all moderators are analysed together. For instance, when I perform the moderators test for muscle strength it is significant; however, when both duration and strength are introduced in the model while the moderator test is significant, only duration reached a significant effect.
Suppose you include two moderators, A and B. The overall test is a test of whether they together account for sufficient variation. The individual test for A is a test of whether, if you already have B in the model, adding A adds anything. Similarly for the test of B. It can happen that, if A and B are closely related that neither A nor B is individually significant but their combination is. Suppose in your case you had two measures of muscle strength, left hand and right hand. Knowing left right adds little since (I assume) they are correlated and vice versa. Together on the other hand they might be massively important. Michael
Thank you very much, Kind regards