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 understanding 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 postestimation 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.

识别基于异质对比表的剂量反应模式是很困难的。可以考虑可能的剂量反应数据生成机制的统计模型的规范，包括其跨研究的变化，对于统计推断至关重要。本文的目的是增加对适用于相关估计表的混合效应剂量反应模型的理解。可以使用带有加法（平均差）和乘法（比值比、风险比）关联度量的命令drmeta。后估计命令drmeta_graph极大地促进了预测平均和研究特定剂量反应关系的可视化。我举例说明了drmeta 的应用 基于非线性和随机效应数据生成机制的实验和观察数据中的回归样条命令，这些机制可以在健康相关科学中遇到。