Meta-regression methods to characterize evidence strength using meaningful-effect percentages conditional on study characteristics.

ABSTRACT: Meta-regression analyses usually focus on estimating and testing differences in average effect sizes between individual levels of each meta-regression covariate in turn. These metrics are useful but have limitations: they consider each covariate individually, rather than in combination, and they characterize only the mean of a potentially heterogeneous distribution of effects. We propose additional metrics that address both limitations. Given a chosen threshold representing a meaningfully strong effect size, these metrics address the questions: "For a given joint level of the covariates, what percentage of the population effects are meaningfully strong?" and "For any two joint levels of the covariates, what is the difference between these percentages of meaningfully strong effects?" We pro

SUBMITTER: Mathur MB 

PROVIDER: S-EPMC8985876 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature