ArticleJournal of cancer education : the official journal of the American Association for Cancer Education2012
Understanding the limits of large datasets.
Article in Journal of cancer education : the official journal of the American Association for Cancer Education, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The utility of medico-legal databases for public health research: a systematic review of peer-reviewed publications using the National Coronial Information System.Health research policy and systems · 2016Pooled it
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- Improving child health through Big Data and data science.Pediatric research · 2023Review
- Risk factors and communities disproportionately affected by cervical cancer in the Russian Federation: A national population-based study.The Lancet regional health. Europe · 2022Article
- Characterization of Traumatic Injury During the Early COVID-19 Pandemic: Results From a National Healthcare Database.Cureus · 2022Article
- Population-Based Study Detailing Cutaneous Melanoma Incidence and Mortality Trends in Canada.Frontiers in medicine · 2022Article
- Artificial Intelligence in Adult Spinal Deformity.Acta neurochirurgica. Supplement · 2022Article
- Incidence and Mortality of Prostate Cancer in Canada during 1992-2010.Current oncology (Toronto, Ont.) · 2021Article
- Incidence trends and survival outcomes of penile squamous cell carcinoma: evidence from the Surveillance, Epidemiology and End Results population-based data.Annals of translational medicine · 2020Article
- Evaluation of a Database for Tracking Cases of Child Sexual Abuse.Psychiatry, psychology, and law : an interdisciplinary journal of the Australian and New Zealand Association of Psychiatry, Psychology and Law · 2017Article
- Identification of patients with congenital hemophilia in a large electronic health record database.Journal of blood medicine · 2017Article
- Identification of people with acquired hemophilia in a large electronic health record database.Journal of blood medicine · 2017Article
- Big data and clinicians: a review on the state of the science.JMIR medical informatics · 2014Review
- Language affects length of stay in emergency departments in Queensland public hospitals.World journal of emergency medicine · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Many health professionals use large datasets to answer behavioral, translational, or clinical questions. Understanding the impact of missing data in large databases, such as disease registries, can avoid erroneous interpretations of these data. Using the California Cancer Registry, the authors selected seven common cancers, seven sociodemographic and clinical variables, and the top three reporting sources, as examples of the type of data that would be deemed critical to most studies. The gender variable had no missing data, followed by age (<0.1 % missing), ethnicity (1.7 %), stage (9.8 %), differentiation (39.1 %), and birthplace (41.1 %). Reports from hospitals and clinics had the lowest percentages of missing data. Users of large datasets should anticipate the limitations of missing data to prevent methodological flaws and misinterpretations of research findings. Knowledge of what and how much data may be missing in large datasets can help prevent errors in research conclusions, while better guiding treatment modalities and public health policies and programs.
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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.