ReviewTranslational lung cancer research2019
Strengths and limitations of large databases in lung cancer radiation oncology research.
Review in Translational lung cancer research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses 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.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Leveraging Radiotherapy Data for Precision Oncology: Veterans Affairs Granular Radiotherapy Information Database.JCO clinical cancer informatics · 2025Article
- Machine Learning Models to Predict Bone Metastasis Risk in Patients With Lung Cancer.Cancer medicine · 2024Article
- The effect of allergies on outcomes following shoulder arthroplasty: A national database analysis of 154,478 patients.Shoulder & elbow · 2024Article
- The contribution of household income to rectal cancer patient characteristics, treatment, and outcomes from 2010 to 2020.Heliyon · 2024Article
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Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
There has been a substantial rise in the utilization of large databases in radiation oncology research. The advantages of these datasets include a large sample size and inclusion of a diverse population of patients in a real-world setting. Such observational studies hold promise in enhancing our understanding of questions for which evidence is conflicting or absent in lung cancer radiotherapy. However, it is critical that investigators understand the strengths and limitations of large databases in order to avoid the common pitfalls that beset observational analyses. This review begins by outlining the data variables available in major registries that are used most often in observational analyses. This is followed by a discussion of the type of radiotherapy-related questions that can be addressed using such datasets, accompanied by examples from the lung cancer literature. Finally, we describe some limitations of observational research and techniques to mitigate bias and confounding. We hope that clinicians and researchers find this review helpful for designing new research studies and interpreting published analyses in the literature.
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