Online Program Home
My Program

Abstract Details

Activity Number: 506 - Categorical Data
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #302937
Title: Network Meta-Regression for Ordinal Outcomes Under Different Links
Author(s): Yeongjin Gwon* and Mo May and Ming-Hui Chen and Zhiyi Chi and Juan Li and Amy Xia and Joseph G Ibrahim
Companies: University of Nebraska Medical Center and Amgen Inc and University of Connecticut and University of Connecticut and Eli Lily and Company and Amgen Inc and UNC
Keywords: Ordinal outcomes; Indirect comparison; Aggregate covariates; Random effects; Goodness-of-fit; Links

In this presentation, we propose a network meta-regression approach for modeling ordinal outcomes under different links. Specifically, we develop regression models based on aggregate trial-level covariates for the underlying cut-off 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 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 meta-data. A case study demonstrating the usefulness of the proposed methodology is carried out using aggregate ordinal outcomes.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program