Network meta-regression for ordinal outcomes: Applications in comparing Crohn's disease treatments

Yeongjin Gwon, May Mo, Ming Hui Chen, Zhiyi Chi, Juan Li, Amy H. Xia, Joseph G. Ibrahim

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1846-1870
Number of pages25
JournalStatistics in Medicine
Volume39
Issue number13
DOIs
StatePublished - Jun 15 2020

Keywords

  • aggregate covariates
  • cut points
  • indirect comparisons
  • model diagnostics
  • proportional odds model
  • random effects

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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