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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