发表机构
Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg; Oslo Center for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo（弗赖堡大学医学院医学生物计量与统计研究所; 奥斯陆大学生物统计系奥斯陆生物统计与流行病学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于多变量网络荟萃分析的框架，结合混合相关模型和VIKOR算法，评估跨结局的治疗获益-危害特征并识别折中方案，开发R包mvnma并应用于两个临床数据集。
AI 中文摘要
卫生技术评估通常旨在平衡多种治疗和结局之间的证据，并评估治疗的获益-危害特征。多变量网络荟萃分析（mvNMA）非常适合这一任务，因为它能够综合跨治疗和结局的证据。然而，mvNMA的潜力一直被忽视，导致决策者依赖于忽略结局相关性的特定结局NMA。基于独立的NMA模型，先前的工作提出了诸如多变量P分数或蛛网图等方法，通过生成跨结局的治疗排序来评估治疗获益-危害特征。在本文中，我们提出了一种基于mvNMA的框架来评估治疗获益-危害特征。我们的框架采用混合相关mvNMA模型，并结合DuMouchel先验用于治疗效果，从而即使某些治疗缺少某些结局的数据，也能估计所有治疗效果。然后，我们将常见的NMA排序指标（包括SUCRA、最佳概率和中位秩）扩展到mvNMA设置。基于这些指标，我们将VIKOR算法适配到mvNMA。这种确定性多准则决策方法量化了治疗的总体和最坏情况表现，并通过平衡这些方面，生成跨结局的排序，同时识别在这两个方面之间提供最优折中的治疗。为便于使用我们的框架，我们开发了R包mvnma。我们将该框架应用于两个临床数据集：一个比较了十一种物理疗法治疗在两种结局上的效果，另一个比较了九种抗抑郁药在五种结局上的效果。我们的框架为评估跨多种结局的治疗获益-危害特征提供了一种替代策略，并鼓励在应用临床和政策环境中采用多变量方法。
英文摘要
Health technology assessment often aims to balance evidence across multiple treatments and outcomes and assess treatment benefit-harm profiles. Multivariate network meta-analysis (mvNMA) is well suited to this task, as it synthesizes evidence across treatments and outcomes. However, the potential of mvNMA has been overlooked, leading decision-makers to rely on outcome-specific NMAs that ignore outcome correlations. Based on independent NMA models, previous work proposed methods such as multivariate P-scores or spie charts to assess treatment benefit-harm profiles by yielding across-outcomes treatment hierarchies. In this article, we propose an mvNMA-based framework for assessing treatment benefit-harm profiles. Our framework uses a hybrid-correlation mvNMA model and incorporates DuMouchel priors for treatment effects, enabling estimation of all treatment effects even when outcomes are missing for some treatments. We then extend common NMA ranking metrics, including SUCRA, the probability of being best, and median ranks, to the mvNMA setting. Based on these metrics, we adapt the VIKOR algorithm for mvNMA. This deterministic multicriteria decision method quantifies overall and worst-case treatment performance and, by balancing these aspects, produces an across-outcomes ranking while identifying treatments offering the optimal compromise between these two aspects. To facilitate use of our framework, we developed the R package mvnma. We applied our framework to two clinical datasets: one comparing eleven physical therapy treatments across two outcomes and another comparing nine antidepressants across five outcomes. Our framework provides an alternative strategy for assessing treatment benefit-harm profiles across multiple outcomes and encourages multivariate approaches in applied clinical and policy settings.
