# What is meta-regression in meta-analysis?

## What is meta-regression in meta-analysis?

Meta-regression refers to a fixed effects model or random effects model that includes one or more study features as covariates. Let y denote a covariate, for instance, y=0 for low risk of bias studies and y=1 for high risk of bias studies.

### What is the purpose of meta-regression?

Meta-regression constitutes an effort to explain statistical heterogeneity in terms of study-level variables, thus summarizing the information not as a single value but as function.

#### What is the difference between subgroup analysis and meta-regression?

A subgroup anal- ysis is performed when the characteristic of interest is a categorical variable (eg, design of the trial as randomized controlled trial or clinical controlled trial). A meta- regression analysis is performed when the characteristic of interest is a metric variable (eg, sample size of the tri- als).

What is the difference between meta-regression and meta-analysis?

Meta-regression is defined to be a meta-analysis that uses regression analysis to combine, compare, and synthesize research findings from multiple studies while adjusting for the effects of available covariates on a response variable.

How many studies do you need for a meta-regression?

Meta-regression should generally not be considered when there are fewer than ten studies in a meta-analysis. Meta-regressions are similar in essence to simple regressions, in which an outcome variable is predicted according to the values of one or more explanatory variables.

## What are betas in regression?

The beta values in regression are the estimated coeficients of the explanatory variables indicating a change on response variable caused by a unit change of respective explanatory variable keeping all the other explanatory variables constant/unchanged.

### What is R2 in meta-regression?

The R2 signifies the amount of heterogeneity in your meta-analysis that can be explained by your moderator variable. If the value recorded is zero, then this implies that the moderator variable has no role in the observed heterogeneity and is likely a non-significant predictor of the outcome concerned.