Evidence map›Paper›PMID 31226413›Full record

ArticleJournal of clinical epidemiology2019

Rapid network meta-analysis using data from Food and Drug Administration approval packages is feasible but with limitations.

Lin Wang, Benjamin Rouse, Arielle Marks-Anglin, Rui Duan, Qiyuan Shi, Kevin Quach, Yong Chen, Christopher Cameron, Christopher H Schmid, Tianjing Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical epidemiology, 2019. 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
–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

2 citing papers in PubMed.

  1. Article
  2. Review
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

10 authors.

Lin WangDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA.
Benjamin RouseDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA.
Arielle Marks-AnglinDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Rui DuanDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Qiyuan ShiDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA.
Kevin QuachDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA.
Yong ChenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Christopher CameronDepartment of Data Analytics and Evidence Synthesis, Cornerstone Research Group Inc., Burlington, ON, Canada.
Christopher H SchmidDepartment of Biostatistics, Brown University School of Public Health, Providence, RI, USA.
Tianjing LiDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA. Electronic address: tli19@jhu.edu.

Funding

Transforming mental health delivery through behavioral economics and implementation scienceP50MH113840 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI MANDELL, DAVID S · 2017 to 2020
$6.6M
Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
Machine Learning Prediction of Cancer SusceptibilityR01LM009012 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H. · 2006 to 2014
$3.3M
Biomedical Computing and Informatics Strategies for Infectious Disease ResearchR01AI116794 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H. · 2016 to 2020
$2.9M
A General Framework to Account for Outcome Reporting Bias in Systematic ReviewsR01LM012607 · NLM · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG · 2017 to 2020
$1.4M
Methods to Accelerate Network Meta-Analysis ProductionR03HS024788 · AHRQ · JOHNS HOPKINS UNIVERSITY · PI LI, TIANJING · 2016 to 2016
$100k
AHRQ HHS R03 HS024788NIAID NIH HHS R01 AI116794NIAID NIH HHS R01 AI130460NIMH NIH HHS P50 MH113840NLM NIH HHS R01 LM009012NLM NIH HHS R01 LM012607
6 · The paper itself

Abstract

objectiveTo test rapid approaches that use Drugs@FDA (a public database of approved drugs) and ClinicalTrials.gov to identify trials and to compare these two sources with bibliographic databases as an evidence base for a systematic review and network meta-analysis (NMA). STUDY DESIGN AND

settingWe searched bibliographic databases, Drugs@FDA, and ClinicalTrials.gov for eligible trials on first-line glaucoma medications. We extracted data, assessed risk of bias, and examined the completeness and consistency of information provided by different sources. We fitted random-effects NMA models separately for trials identified from each source and for all unique trials from three sources.

resultsWe identified 138 unique trials including 29,394 participants on 15 first-line glaucoma medications. For a given trial, information reported was sometimes inconsistent across data sources. Journal articles provided the most information needed for a systematic review; trial registrations provided the least. Compared to an NMA including all unique trials, we were able to generate reasonably precise effect estimates and similar relative rankings for available interventions using trials from Drugs@FDA alone (but not ClinicalTrials.gov).

conclusionsA rapid NMA approach using data from Drugs@FDA is feasible but has its own limitations. Reporting of trial design and results can be improved in both the drug approval packages and on ClinicalTrials.gov.

Indexed as

Clinical Trials as TopicDrug ApprovalNetwork Meta-Analysis as TopicUnited States Food and Drug AdministrationComparative Effectiveness ResearchDatabases, FactualFeasibility StudiesGlaucomaHumansPublication BiasRegistriesSystematic Reviews as TopicUnited StatesClinical trialClinicalTrials.govComparative-effectiveness researchDrugs@FDANetwork meta-analysisRapid systematic review

Identifiers

PMID31226413
PMCPMC6773505

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.