ReviewRheumatology (Oxford, England)2020
Adding value to real-world data: the role of biomarkers.
Review in Rheumatology (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The evidence base of interventions to treat antenatal depression: a meta-analysis of randomized controlled trials.Archives of women's mental health · 2026Pooled it
- The tumor immune microenvironment: implications for cancer immunotherapy, treatment strategies, and monitoring approaches.Frontiers in immunology · 2025Review
- Characteristics and Survival Outcomes of Patients With MetastaticJCO precision oncology · 2024Article
- Machine learning-based remission prediction in rheumatoid arthritis patients treated with biologic disease-modifying anti-rheumatic drugs: findings from the Kuwait rheumatic disease registry.Frontiers in big data · 2024Article
- Prevalence of female genital mutilation and associated factors among daughters aged 0-14 years in sub-Saharan Africa: a multilevel analysis of recent demographic health surveys.Frontiers in reproductive health · 2023Article
- Milestones of Precision Medicine: An Innovative, Multidisciplinary Overview.Molecular diagnosis & therapy · 2021Review
- Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics.Arthritis research & therapy · 2021Article
- Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics.Scientific reports · 2021Observational
- Real-World Evidence: Bridging Gaps in Evidence to Guide Payer Decisions.PharmacoEconomics - open · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Adding biomarker information to real world datasets (e.g. biomarker data collected into disease/drug registries) can enhance mechanistic understanding of intra-patient differences in disease trajectories and differences in important clinical outcomes. Biomarkers can detect pathologies present early in disease potentially paving the way for preventative intervention strategies, which may help patients to avoid disability, poor treatment outcome, disease sequelae and premature mortality. However, adding biomarker data to real world datasets comes with a number of important challenges including sample collection and storage, study design and data analysis and interpretation. In this narrative review we will consider the benefits and challenges of adding biomarker data to real world datasets and discuss how biomarker data have added to our understanding of complex diseases, focusing on rheumatoid arthritis.
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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.