Evidence map›Paper›PMID 31492773›Full record

ArticleBMJ open2019

Empirical evaluation of SUCRA-based treatment ranks in network meta-analysis: quantifying robustness using Cohen's kappa.

Caitlin H Daly, Binod Neupane, Joseph Beyene, Lehana Thabane, Sharon E Straus, Jemila S Hamid

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In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 27 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 27 pooled it
–field-weighted citation impact
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

44 citing papers in PubMed, 27 syntheses or guidelines pooled it.

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  5. Complications of Alloplastic Graft Materials Used in Cranioplasty: Systematic Review and Network Meta-Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2026
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  11. Combined exercise training to improve gait in older stroke patients: a systematic review and network meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025
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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

6 authors.

Caitlin H DalyDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Binod NeupaneDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Joseph BeyeneDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Lehana ThabaneDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Sharon E StrausKnowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.
Jemila S HamidDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada jehamid@cheo.on.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo provide a framework for quantifying the robustness of treatment ranks based on Surface Under the Cumulative RAnking curve (SUCRA) in network meta-analysis (NMA) and investigating potential factors associated with lack of robustness.

methodsWe propose the use of Cohen's kappa to quantify the agreement between SUCRA-based treatment ranks estimated through NMA of a complete data set and a subset of it. We illustrate our approach using five published NMA data sets, where robustness was assessed by removing studies one at a time.

resultsOverall, SUCRA-based treatment ranks were robust to individual studies in the five data sets we considered. We observed more incidences of disagreement between ranks in the networks with larger numbers of treatments. Most treatments moved only one or two ranks up or down. The lowest quadratic weighted kappa estimate observed across all networks was in the network with the smallest number of treatments (4), where weighted kappa=40%. In the network with the largest number of treatments (12), the lowest observed quadratic weighted kappa=89%, reflecting a small shift in this network's treatment ranks overall. Preliminary observations suggest that a study's size, the number of studies making a treatment comparison, and the agreement of a study's estimated treatment effect(s) with those estimated by other studies making the same comparison(s) may explain the overall robustness of treatment ranks to studies.

conclusionsInvestigating robustness or sensitivity in an NMA may reveal outlying rank changes that are clinically or policy-relevant. Cohen's kappa is a useful measure that permits investigation into study characteristics that may explain varying sensitivity to individual studies. However, this study presents a framework as a proof of concept and further investigation is required to identify potential factors associated with the robustness of treatment ranks using more extensive empirical evaluations.

Indexed as

BiostatisticsNetwork Meta-Analysis as TopicData CollectionData Interpretation, StatisticalHumansOutcome Assessment, Health Carekappamixed-treatment comparisonsnetwork meta-analysisranksrobustnessSUCRA

Identifiers

PMID31492773
PMCPMC6731799

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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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.