Contents
Which is an example of a meta regression?
Meta-regression, although it has its own limitations, can be a very powerful tool in meta-analyses. It is also very versatile: multiple meta-regression, for example, allows us to include not only one, but several predictor variables, along with their interaction.
Why do we need mixed effect model in meta regression?
In meta-regression, we also have to make sure that the model pays more attention to studies with a lower sampling error, since we can assume that their estimates are closer to the “truth”. Meta-regression achieves this by assuming a mixed-effects model.
Can a meta regression model be used without an intercept?
A question that comes up frequently is the proper interpretation of meta-regression models with or without an intercept term. Below I illustrate the difference using the dataset for the BCG vaccine meta-analysis (Colditz et al., 1994). The dataset is called dat.bcg.
What does the BTT argument in METAFOR mean?
The btt argument stands for “betas to test” and is used to specify which coefficients we want include in the test. A different way to conduct the same test is to use the L argument, which allows us to specify one or more vectors of numbers, which are multiplied with the model coefficients.
How does a meta regression model predict effect size?
Based on this information, a meta-regression model tries to predict y y, the study’s effect size. The fact that effect sizes are used as predicted variables, however, adds some complexity.
What happens when predictors in a regression are highly correlated?
It appears as if, when predictors are highly correlated, the answers you get depend on the predictors in the model. That’s not good! Let’s proceed through the table and in so doing carefully summarize the effects of multicollinearity on the regression analyses.
What is the fixed value of β in meta regression?
This fixed value of β β is the estimated difference in effect sizes between two subgroups. When people speak of a “meta-regression”, however, they usually think of models in which a continuous variable was used as the predictor. This brings us back the generic meta-regression formula shown in equation 8.2.
Do you have to take sampling error into account in a meta regression?
In “normal” meta-analyses, we take this into account by giving studies a smaller or higher weight. In meta-regression, we also have to make sure that the model pays more attention to studies with a lower sampling error, since we can assume that their estimates are closer to the “truth”.