A key output of network meta-analysis (NMA) is the relative ranking of the treatments; nevertheless, it has attracted a lot of criticism. This is mainly due to the fact that ranking is an influential output and prone to over-interpretations even when relative effects imply small differences between treatments. To date, common ranking methods rely on metrics that lack a straightforward interpretation, while it is still unclear how to measure their uncertainty. We introduce a novel framework for estimating treatment hierarchies in NMA. At first, we formulate a mathematical expression that defines a treatment choice criterion (TCC) based on clinically important values. This TCC is applied to the study treatment effects to generate paired data indicating treatment preferences or ties. Then, we synthesize the paired data across studies using an extension of the so-called "Bradley-Terry" model. We assign to each treatment a latent variable interpreted as the treatment "ability" and we estimate the ability parameters within a regression model. Higher ability estimates correspond to higher positions in the final ranking. We further extend our model to adjust for covariates that may affect treatment selection. We illustrate the proposed approach and compare it with alternatives in two datasets: a network comparing 18 antidepressants for major depression and a network comparing 6 antihypertensives for the incidence of diabetes. Our approach provides a robust and interpretable treatment hierarchy which accounts for clinically important values and is presented alongside with uncertainty measures. Overall, the proposed framework offers a novel approach for ranking in NMA based on concrete criteria and preserves from over-interpretation of unimportant differences between treatments.

翻译：网络荟萃分析（NMA）的一个关键输出是治疗的相对排序，然而这一方法备受争议。这主要是因为排序是具有影响力的输出结果，即使治疗间的相对效应差异很小，也容易被过度解读。迄今为止，常见的排序方法依赖于缺乏直观解释的度量指标，且其不确定性的衡量方式仍不明确。本文提出了一种用于估计NMA中治疗等级体系的新框架。首先，我们基于临床重要值构建了定义治疗选择准则（TCC）的数学表达式。将该TCC应用于研究中的治疗效果数据，可生成指示治疗偏好或等效关系的配对数据。随后，我们通过扩展"Bradley-Terry"模型对跨研究的配对数据进行综合。我们为每种治疗分配一个被解释为治疗"能力"的潜变量，并通过回归模型估计能力参数。更高的能力估计值对应最终排序中更靠前的位置。我们进一步扩展模型以调整可能影响治疗选择的协变量。通过两个数据集（一个比较18种抗抑郁药治疗重度抑郁的网络，以及一个比较6种降压药对糖尿病发病率的网络）演示了所提出的方法，并与替代方法进行了比较。我们的方法提供了稳健且可解释的治疗等级体系，该体系综合考虑了临床重要值，并附带了不确定性度量。总体而言，所提出的框架为NMA中的排序提供了一种基于具体准则的新方法，并避免了对治疗间非重要差异的过度解读。