|Title||Network meta-regression for ordinal outcomes: Applications in comparing Crohn's disease treatments.|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Gwon, Yeongjin, May Mo, Ming-Hui Chen, Zhiyi Chi, Juan Li, Amy H. Xia, and Joseph G. Ibrahim|
|Date Published||2020 Mar 12|
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.
|Alternate Journal||Stat Med|
|Original Publication||Network meta-regression for ordinal outcomes: Applications in comparing Crohn's disease treatments.|
|PubMed Central ID||PMC7727029|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 GM070335 / GM / NIGMS NIH HHS / United States
U54 GM115458 / GM / NIGMS NIH HHS / United States
P01CA142538 / NH / NIH HHS / United States
Network meta-regression for ordinal outcomes: Applications in comparing Crohn's disease treatments.