Fellow Statalisters,
I am performing a meta-regression analysis using 'metareg.' When no covariates are in the model, the intercept should be the exact equivalent of a random-effect pooled estimate calculated with 'meta.'
Now, the purpose of these models are: [1] to look at predictors of the effect estimate (or in other words, to ceck if any study-feature makes studies more likely to show a bigger or smaller effect size); [2] ...here is my doubt: how should we interpret the intercept in a metaregression model with covariates? Should it be considered as a pooled estimate adjusted for study-features, the same way we would adjust for confounders, say, in a logistic regression model? I have been taking a look in books and articles on the subject but have not been able so far to come up with a reasonably justified answer. In case anyone has a suggestion on it and the answer is 'yes, it is like to adjust,' I would be very much interested in having a reference (for meta-analysis with binary outcomes) in order to 'see' or figure out how the model works in order to operate such 'adjustement' for confounders.
Any contribution or suggestion will be greatly appreciated.
Thanks,
Ron
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