Effect size can differ as a function of the elapsed time since treatment or as a function of other key covariates, such as sex or age. In evidence synthesis, a better understanding of the precise conditions under which treatment does work or does not work well has been highly valued. With increasingly accessible individual patient or participant data (IPD), more precise and informative inference can be within our reach. However, simultaneously combining multiple related parameters across heterogeneous studies is challenging because each parameter from each study has a specific interpretation within the context of the study and other covariates in the model. This paper proposes a novel mapping method to combine study-specific estimates of multiple related parameters across heterogeneous studies, which ensures valid inference at all inference levels by combining sample-dependent functions known as Confidence Distributions (CD). We describe the "CD-based mapping method" and provide a data application example for a multivariate random-effects meta-analysis model. We estimated up to 13 study-specific regression parameters for each of 14 individual studies using IPD in the first step, and subsequently combined the study-specific vectors of parameters, yielding a full vector of hyperparameters in the second step of meta-analysis. Sensitivity analysis indicated that the CD-based mapping method is robust to model misspecification. This novel approach to multi-parameter synthesis provides a reasonable methodological solution when combining complex evidence using IPD.

中文翻译：

---

一种基于 CD 的映射方法，用于组合来自异构干预试验的多个相关参数

效果大小可以根据治疗后经过的时间或其他关键协变量（例如性别或年龄）的函数而有所不同。在证据综合中，对治疗有效或无效的确切条件的更好理解受到高度重视。随着越来越容易获得的个体患者或参与者数据 (IPD)，我们可以实现更精确和信息量更大的推断。然而，在异质研究中同时结合多个相关参数是具有挑战性的，因为每项研究的每个参数在研究背景和模型中的其他协变量中都有特定的解释。本文提出了一种新的映射方法，将跨异质研究的多个相关参数的研究特定估计结合起来，通过组合称为置信分布 (CD) 的样本相关函数，确保在所有推理级别进行有效推理。我们描述了“基于 CD 的映射方法”，并为多元随机效应荟萃分析模型提供了一个数据应用示例。我们在第一步中使用 IPD 为 14 项单独研究中的每项估计了多达 13 个研究特定的回归参数，然后结合研究特定的参数向量，在元分析的第二步中产生一个完整的超参数向量。敏感性分析表明，基于 CD 的映射方法对模型指定错误具有鲁棒性。当使用 IPD 组合复杂证据时，这种多参数合成的新方法提供了合理的方法论解决方案。