Hi. I forwarded this to one of the statisticians.

The statistician replies:

"The goal of meta-regression is not to compare different models but to see how much variation of effect sizes could be explained by a certain set of predictors. That's the reason why we did not add those goodness-of-fit statistics.

"If the customer is really interested in comparing models, I believe he or she may resort to the other procedures like REGRESSION or GLM by specifying the effect size estimates as a target or dependent variable."

What you want todo is to see whether the addition of components improve the explanation of the variance in the effect sizes. it is not standard procedure, yet, but is an elegant way to create the best explanatory model, and to report the estimates based on that best model, I think?

The statistician replies:

"The goal of meta-regression is not to compare different models but to see how much variation of effect sizes could be explained by a certain set of predictors. That's the reason why we did not add those goodness-of-fit statistics.

"If the customer is really interested in comparing models, I believe he or she may resort to the other procedures like REGRESSION or GLM by specifying the effect size estimates as a target or dependent variable."

The statistician's response:

"I agree that the model selection feature could be a future direction to enhance the Meta-regression procedure. But for now, we do not support in META REGRESSION."

What you want todo is to see whether the addition of components improve the explanation of the variance in the effect sizes. it is not standard procedure, yet, but is an elegant way to create the best explanatory model, and to report the estimates based on that best model, I think?