Meta regression is a statistical technique used to analyze the results of previous research studies in order to identify factors that may have influenced the results. This type of analysis can be helpful in understanding the reasons behind inconsistencies or variations in study results, and can provide valuable insights for future research.

For example, consider a meta regression analysis of studies investigating the effectiveness of a particular drug for treating depression. The meta regression analysis may find that the effectiveness of the drug varies depending on the severity of the depression, with more severe cases showing greater improvement. This information can be used to better tailor treatment plans and improve outcomes for patients.

Another example of meta regression analysis is in the field of economics. A meta regression analysis of studies examining the relationship between government spending and economic growth may find that the impact of government spending on economic growth varies depending on the type of spending and the stage of the economic cycle. This information can be used to inform policy decisions and improve economic outcomes.

In both of these examples, meta regression analysis allows for a more in-depth understanding of the factors that influence the results of research studies. By taking into account a variety of factors, meta regression analysis provides a more comprehensive view of the results and can help researchers and policymakers make more informed decisions.