Evidence map›Paper›PMID 30449954›Full record

ArticleJournal of the Royal Statistical Society. Series A, (Statistics in Society)2018

Sensitivity of treatment recommendations to bias in network meta-analysis.

David M Phillippo, Sofia Dias, A E Ades, Vanessa Didelez, Nicky J Welton

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Statistical Society. Series A, (Statistics in Society), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 12 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

25 citing papers in PubMed, 12 syntheses or guidelines pooled it.

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  13. Network Meta-Analysis with Class Effects: A Practical Guide and Model Selection Algorithm.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
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  19. Review
  20. Adjusting trial results for biases in meta-analysis: combining data-based evidence on bias with detailed trial assessment.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2020
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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

5 authors.

David M PhillippoUniversity of Bristol UK.
Sofia DiasUniversity of Bristol UK.
A E AdesUniversity of Bristol UK.
Vanessa DidelezLeibniz Institute for Prevention Research and Epidemiology, and University of Bremen Germany.
Nicky J WeltonUniversity of Bristol UK.

Funding

Medical Research Council MC_U145079307Medical Research Council MR/K025643/1Medical Research Council MR/M005232/1Medical Research Council MR/P015298/1
6 · The paper itself

Abstract

Network meta-analysis (NMA) pools evidence on multiple treatments to estimate relative treatment effects. Included studies are typically assessed for risk of bias; however, this provides no indication of the impact of potential bias on a decision based on the NMA. We propose methods to derive bias adjustment thresholds which measure the smallest changes to the data that result in a change of treatment decision. The methods use efficient matrix operations and can be applied to explore the consequences of bias in individual studies or aggregate treatment contrasts, in both fixed and random-effects NMA models. Complex models with multiple types of data input are handled by using an approximation to the hypothetical aggregate likelihood. The methods are illustrated with a simple NMA of thrombolytic treatments and a more complex example comparing social anxiety interventions. An accompanying R package is provided.

Indexed as

Evidence synthesisInfluence matrixMixed treatment comparisonQuality of evidenceRisk of biasThreshold analysis

Identifiers

PMID30449954
PMCPMC6221150

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.