ArticleEGEMS (Washington, DC)2015
Transparent reporting of data quality in distributed data networks.
Article in EGEMS (Washington, DC), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
77 citing papers in PubMed, 3 syntheses or guidelines pooled it, 125 citations in OpenAlex.
- Error rates of data processing methods in clinical research: A systematic review and meta-analysis of manuscripts identified through PubMed.International journal of medical informatics · 2025Pooled it
- Pooled it
- The Minimum Data Set for Rare Diseases: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Hyperbaric oxygen in the artificial intelligence era: integration and innovation.Medical gas research · 2027Review
- A generative approach for semantic auditing of electronic health records.NPJ digital medicine · 2026Article
- From data to decisions: a modular platform for modelling and simulation of infectious disease diffusion in networks.BMC medical informatics and decision making · 2026Article
- Assessing the harmonization of structured electronic health record data to reference terminologies and data completeness through data provenance.Learning health systems · 2025Article
- Uncertainties in outcome modelling in radiation oncology.Physics and imaging in radiation oncology · 2025Review
- Assessing and improving research readiness in PCORnet®.Journal of clinical and translational science · 2025Article
- The Synergy of Machine Learning and Epidemiology in Addressing Carbapenem Resistance: A Comprehensive Review.Antibiotics (Basel, Switzerland) · 2024Review
- Systematically assessing the quality of dental electronic health record data for an investigation into oral health care disparities.Journal of public health dentistry · 2024Article
- Assessing Real-World Data From Electronic Health Records for Health Technology Assessment: The SUITABILITY Checklist: A Good Practices Report of an ISPOR Task Force.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2024Article
- A national unmet needs assessment for CTSA-affiliated electronic health record data networks: A customer discovery approach.Journal of clinical and translational science · 2024Article
- Error Rates of Data Processing Methods in Clinical Research: A Systematic Review and Meta-Analysis of Manuscripts Identified Through PubMed.Research square · 2023Article
- Observational studies of treatment effectiveness in neurology.Brain : a journal of neurology · 2023Review
- Data Management 101 for drug developers: A peek behind the curtain.Clinical and translational science · 2023Review
- Automating Electronic Health Record Data Quality Assessment.Journal of medical systems · 2023Review
- Collaborative Program to Evaluate Real World Data for Use in Clinical Studies and Regulatory Decision Making.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Translating periodontal data to knowledge in a learning health system.Journal of the American Dental Association (1939) · 2022Article
- The IeDEA harmonist data toolkit: A data quality and data sharing solution for a global HIV research consortium.Journal of biomedical informatics · 2022Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 9 institutions in 2 countries.
Funding
Abstract
introductionPoor data quality can be a serious threat to the validity and generalizability of clinical research findings. The growing availability of electronic administrative and clinical data is accompanied by a growing concern about the quality of these data for observational research and other analytic purposes. Currently, there are no widely accepted guidelines for reporting quality results that would enable investigators and consumers to independently determine if a data source is fit for use to support analytic inferences and reliable evidence generation. MODEL AND
methodsWe developed a conceptual model that captures the flow of data from data originator across successive data stewards and finally to the data consumer. This "data lifecycle" model illustrates how data quality issues can result in data being returned back to previous data custodians. We highlight the potential risks of poor data quality on clinical practice and research results. Because of the need to ensure transparent reporting of a data quality issues, we created a unifying data-quality reporting framework and a complementary set of 20 data-quality reporting recommendations for studies that use observational clinical and administrative data for secondary data analysis. We obtained stakeholder input on the perceived value of each recommendation by soliciting public comments via two face-to-face meetings of informatics and comparative-effectiveness investigators, through multiple public webinars targeted to the health services research community, and with an open access online wiki. RECOMMENDATIONS: Our recommendations propose reporting on both general and analysis-specific data quality features. The goals of these recommendations are to improve the reporting of data quality measures for studies that use observational clinical and administrative data, to ensure transparency and consistency in computing data quality measures, and to facilitate best practices and trust in the new clinical discoveries based on secondary use of observational data.
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What OpenQuestion holds
Registered trials
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.