Why do we need mixed effect model in meta regression?

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.

How is the meta-analytic random-effects model conceptualized?

In essence, the meta-analytic random-effects model can be conceptualized as a multilevel model with the true effects at level 2 and the observed effects at level 1. Using typical multilevel model terminology, the random = ~ 1 | trial argument adds random intercepts at level 2 to the model.

How to do a multilevel metaregression in R?

Using the metafor package, I use rma.mv and the random argument to account for the dependency: where the source denotes the study, with six levels, and the compare denotes the measure, with two levels.

How are random effects fitted to rma.uni ( ) function?

A random-effects model can be fitted to these data using the rma.uni () function with: The default for the method argument is to use REML estimation for the amount of heterogeneity and a random-effects model is then automatically fitted. When using the rma.mv () function, random effects must be explicitly added to the model via the random argument.

Which is the outcome variable in meta regression?

In meta-regression, the outcome variable is the effect estimate (for example, a mean difference, a risk difference, a log odds ratio or a log risk ratio). The explanatory variables are characteristics of studies that might influence the size of intervention effect.

When to use mixed effect logistic regression in Stata?

Version info: Code for this page was tested in Stata 12.1 Mixed effects logistic regression is used to model binary outcome variables, in which the log odds of the outcomes are modeled as a linear combination of the predictor variables when data are clustered or there are both fixed and random effects.

How is meta regression used in subgroup analysis?

Meta-regression can also be used to investigate differences for categorical explanatory variables as done in subgroup analyses. If there are J subgroups membership of particular subgroups is indicated by using J – 1 dummy variables (which can only take values of zero or one) in the meta-regression model (as in standard linear regression modelling).

How is the variable X used in meta regression?

In meta-regression, this logic is applied to entire studies. The variable x x represents characteristics of studies, for example the year in which it was conducted. Based on this information, a meta-regression model tries to predict y y, the study’s effect size.

How to test multiple factors and their interaction?

The example below shows how to test/examine multiple factors and their interaction in (mixed-effects) meta-regression models.

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”.

What is the formula for a meta regression?

A standard regression equation, therefore, looks like this: In meta-regression, the variable y y we want to predict is the observed effect size ^θk θ ^ k of study k k. The formula for a meta-regression looks similar to the one of a normal regression model:

Why are subgroup analyses used in meta regression?

As we learned, subgroup analyses shift the focus of our analyses away from finding one overall effect. Instead, they allow us to investigate patterns of heterogeneity in our data, and what causes them. We also mentioned that subgroup analyses are a special form of meta-regression.