Crohn's disease (CD) is a life-long condition associated with recurrent relapses characterized by abdominal pain, weight loss, anemia, and persistent diarrhea. In the US, there are approximately 780 000 CD patients and 33 000 new cases added each year. In this article, we propose a new network meta-regression approach for modeling ordinal outcomes in order to assess the efficacy of treatments for CD. Specifically, we develop regression models based on aggregate covariates for the underlying cut points of the ordinal outcomes as well as for the variances of the random effects to capture heterogeneity across trials. Our proposed models are particularly useful for indirect comparisons of multiple treatments that have not been compared head-to-head within the network meta-analysis framework. Moreover, we introduce Pearson residuals and construct an invariant test statistic to evaluate goodness-of-fit in the setting of ordinal outcome data. A detailed case study demonstrating the usefulness of the proposed methodology is carried out using aggregate ordinal outcome data from 16 clinical trials for treating CD.

中文翻译：

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序数结果的网络元回归：在比较克罗恩病治疗中的应用。


克罗恩病 (CD) 是一种与复发相关的终生疾病，其特征为腹痛、体重减轻、贫血和持续性腹泻。在美国，大约有 780,000 名克罗恩病患者，每年新增病例 33,000 例。在本文中，我们提出了一种新的网络元回归方法，用于对顺序结果进行建模，以评估 CD 治疗的疗效。具体来说，我们开发了基于聚合协变量的回归模型，用于顺序结果的基本切点以及随机效应的方差，以捕获试验之间的异质性。我们提出的模型对于尚未在网络荟萃分析框架内进行面对面比较的多种治疗方法的间接比较特别有用。此外，我们引入皮尔逊残差并构建不变检验统计量来评估序数结果数据设置中的拟合优度。使用来自 16 项治疗 CD 的临床试验的汇总序数结果数据进行了详细的案例研究，证明了所提出的方法的有用性。