Evidence map›Paper›PMID 32166784›Full record

ArticleStatistics in medicine2020

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

Open access · greenAbstract read
In one paragraph

Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 1 country.

Yeongjin GwonDepartment of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska, USA.ORCID https://orcid.org/0000-0001-6974-337X
May MoAmgen Inc., Thousand Oaks, California, USA.
Ming-Hui ChenDepartment of Statistics, University of Connecticut, Storrs, Connecticut, USA.ORCID https://orcid.org/0000-0003-1935-2447
Zhiyi ChiDepartment of Statistics, University of Connecticut, Storrs, Connecticut, USA.
Juan LiLily Biotechnology Center, Eli Lily and Company, San Diego, California, USA.
Amy H XiaAmgen Inc., Thousand Oaks, California, USA.
Joseph G IbrahimDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.ORCID https://orcid.org/0000-0003-2428-6552
Amgen (United States) · USUniversity of Connecticut · USElSohly Laboratories (United States) · USUniversity of Nebraska Medical Center · USUniversity of North Carolina at Chapel Hill · US

Funding

Tracking and Evaluation CoreU54GM115458 · NIGMS · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI RIZZO, MATTHEW · 2016 to 2025
$42.8M
Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVIDIAN, MARIE, KOSOROK, MICHAEL R · 2010 to 2019
$21.3M
Bayesian Approaches to Model Selection for Survival DataR01GM070335 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI IBRAHIM, JOSEPH G · 2003 to 2019
$4.0M
NCI NIH HHS P01 CA142538NIGMS NIH HHS R01 GM070335NIGMS NIH HHS U54 GM115458NIH HHS P01CA142538
6 · The paper itself

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.

Indexed as

aggregate covariatescut pointsindirect comparisonsmodel diagnosticsproportional odds modelrandom effects

Identifiers

PMID32166784
PMCPMC7727029
OpenAlexW3010938713

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.