Jiao Yang, Mun Eun-Young, Trikalinos Thomas A, Xie Minge
Department of Statistics, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
Department of Health Behavior and Health Systems, University of North Texas Health Science Center, Fort Worth, TX, USA.
Stat Interface. 2020;13(4):533-549. doi: 10.4310/sii.2020.v13.n4.a10.
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.
效应大小可能会因治疗后的时间推移而有所不同，也可能因其他关键协变量（如性别或年龄）而有所不同。在证据综合中，更好地理解治疗有效或效果不佳的精确条件一直备受重视。随着个体患者或参与者数据（IPD）越来越容易获取，更精确和信息丰富的推断触手可及。然而，在异质性研究中同时合并多个相关参数具有挑战性，因为每个研究中的每个参数在研究背景和模型中的其他协变量方面都有特定的解释。本文提出了一种新颖的映射方法，用于在异质性研究中合并多个相关参数的特定研究估计值，该方法通过合并称为置信分布（CD）的样本依赖函数，确保在所有推断水平上进行有效的推断。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。第一步，我们使用IPD为14项个体研究中的每一项估计了多达13个特定研究的回归参数，随后在荟萃分析的第二步中合并了特定研究的参数向量，得出了一个超参数的完整向量。敏感性分析表明，基于CD的映射方法对模型误设具有稳健性。这种多参数综合的新方法在使用IPD合并复杂证据时提供了一个合理的方法学解决方案。
