Meta-regression uses different methods to find the best model for analyzing data in a meta-analysis. Instead of traditional testing methods, information-theoretic approaches are more effective in selecting the most accurate model. These approaches outperform conventional methods and have a higher chance of identifying the true model in various scenarios. They can be used with both maximum likelihood and restricted maximum likelihood estimation methods, with the latter sometimes providing even better results. This suggests that information-theoretic approaches should be used more often in meta-regression analysis.