Recognizing a dose–response pattern based on heterogeneous tables of contrasts is hard. Specification of a statistical model that can consider the possible dose–response data-generating mechanism, including its variation across studies, is crucial for statistical inference. The aim of this article is to increase the un- derstanding of mixed-effects dose–response models suitable for tables of correlated estimates. One can use the command drmeta with additive (mean difference) and multiplicative (odds ratios, hazard ratios) measures of association. The postes- timation command drmeta graph greatly facilitates the visualization of predicted average and study-specific dose–response relationships. I illustrate applications of the drmeta command with regression splines in experimental and observational data based on nonlinear and random-effects data-generation mechanisms that can be encountered in health-related sciences
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