Evidence map›Paper›PMID 27430731›Full record

ArticleJournal of clinical epidemiology2016

A threshold analysis assessed the credibility of conclusions from network meta-analysis.

Deborah M Caldwell, A E Ades, Sofia Dias, Sarah Watkins, Tianjing Li, Nichole Taske, Bhash Naidoo, Nicky J Welton

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 3 pooled it
3.5field-weighted citation impact, top 6% 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

11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 29 citations in OpenAlex.

  1. Pooled it
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  11. Sensitivity of treatment recommendations to bias in network meta-analysis.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2018
    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

8 authors at 3 institutions in 2 countries.

Deborah M CaldwellSchool of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK. Electronic address: d.m.caldwell@bristol.ac.uk.
A E AdesSchool of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK.
Sofia DiasSchool of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK.
Sarah WatkinsSchool of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK.
Tianjing LiDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA.
Nichole TaskeCentre for Clinical Practice, National Institute for Health and Care Excellence, 10 Spring Gardens, London SW1A 2BU, UK.
Bhash NaidooCentre for Clinical Practice, National Institute for Health and Care Excellence, 10 Spring Gardens, London SW1A 2BU, UK.
Nicky J WeltonSchool of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK.
University of Bristol · GBNational Institute for Health and Care Excellence · GBJohns Hopkins University · US

Funding

British Heart FoundationMedical Research Council G0902118Medical Research Council MC_U145079307Medical Research Council MR/K025643/1Medical Research Council MR/KO232331/1Medical Research Council MR/M005615/1Wellcome Trust
6 · The paper itself

Abstract

objectiveTo assess the reliability of treatment recommendations based on network meta-analysis (NMA). STUDY DESIGN AND

settingWe consider evidence in an NMA to be potentially biased. Taking each pairwise contrast in turn, we use a structured series of threshold analyses to ask: (1) "How large would the bias in this evidence base have to be before it changed our decision?" and (2) "If the decision changed, what is the new recommendation?" We illustrate the method via two NMAs in which a Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessment for NMAs has been implemented: weight loss and osteoporosis.

resultsFour of the weight-loss NMA estimates were assessed as "low" and six as "moderate" quality by GRADE; for osteoporosis, six were "low," nine were "moderate," and 1 was "high." The threshold analysis suggests plausible bias in 3 of 10 estimates in the weight-loss network could have changed the treatment recommendation. For osteoporosis, plausible bias in 6 of 16 estimates could change the recommendation. There was no relation between plausible bias changing a treatment recommendation and the original GRADE assessments.

conclusionsReliability judgments on individual NMA contrasts do not help decision makers understand whether a treatment recommendation is reliable. Threshold analysis reveals whether the final recommendation is robust against plausible degrees of bias in the data.

Indexed as

Network Meta-Analysis as TopicBiasComparative Effectiveness ResearchHumansOsteoporosisOutcome Assessment, Health CareReproducibility of ResultsWeight Reduction ProgramsBiasComparative effectivenessGRADEHealth technology assessmentMixed treatment comparisonQuality assessmentReliability

Identifiers

PMID27430731
PMCPMC5176010
OpenAlexW2503590158

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.