Evidence map›Paper›PMID 33448252›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2021

Joining the Dots: Linking Disconnected Networks of Evidence Using Dose-Response Model-Based Network Meta-Analysis.

Hugo Pedder, Sofia Dias, Meg Bennetts, Martin Boucher, Nicky J Welton

Abstract readNetwork Meta-Analysis
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 6 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

18 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Optimal dose and type of exercise improve the overall balance in adults with Parkinson's disease: a systematic review and Bayesian network meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025
    Pooled it
  5. Pooled it
  6. Pooled it
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Network meta-analysis with dose-response relationships.BMC medical research methodology · 2026
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. 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

5 authors.

Hugo PedderDepartment of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID 0000-0002-7813-3749
Sofia DiasCentre for Reviews and Dissemination, University of York, York, North Yorkshire, UK.ORCID 0000-0002-2172-0221
Meg BennettsPharmacometrics, Pfizer Ltd, Sandwich, Kent, UK.
Martin BoucherPharmacometrics, Pfizer Ltd, Sandwich, Kent, UK.
Nicky J WeltonDepartment of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

Funding

Department of HealthMedical Research Council MR/K025643/1Medical Research Council MR/M005615/1Medical Research Council MR/R025223/1
6 · The paper itself

Abstract

backgroundNetwork meta-analysis (NMA) synthesizes direct and indirect evidence on multiple treatments to estimate their relative effectiveness. However, comparisons between disconnected treatments are not possible without making strong assumptions. When studies including multiple doses of the same drug are available, model-based NMA (MBNMA) presents a novel solution to this problem by modeling a parametric dose-response relationship within an NMA framework. In this article, we illustrate several scenarios in which dose-response MBNMA can connect and strengthen evidence networks.

methodsWe created illustrative data sets by removing studies or treatments from an NMA of triptans for migraine relief. We fitted MBNMA models with different dose-response relationships. For connected networks, we compared MBNMA estimates with NMA estimates. For disconnected networks, we compared MBNMA estimates with NMA estimates from an "augmented" network connected by adding studies or treatments back into the data set.

resultsIn connected networks, relative effect estimates from MBNMA were more precise than those from NMA models (ratio of posterior SDs NMA v. MBNMA: median = 1.13; range = 1.04-1.68). In disconnected networks, MBNMA provided estimates for all treatments where NMA could not and were consistent with NMA estimates from augmented networks for 15 of 18 data sets. In the remaining 3 of 18 data sets, a more complex dose-response relationship was required than could be fitted with the available evidence.

conclusionsWhere information on multiple doses is available, MBNMA can connect disconnected networks and increase precision while making less strong assumptions than alternative approaches. MBNMA relies on correct specification of the dose-response relationship, which requires sufficient data at different doses to allow reliable estimation. We recommend that systematic reviews for NMA search for and include evidence (including phase II trials) on multiple doses of agents where available.

Indexed as

HumansdisconnecteddoseMBNMAmeta-analysisnetworkNMAsynthesis

Identifiers

PMID33448252
PMCPMC7879230

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.