ArticleCurrent epidemiology reports2019
Sampling and Sampling Frames in Big Data Epidemiology.
Article in Current epidemiology reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed.
- Towards a unified approach to formal "risk of bias" assessments for causal and descriptive inference.Quality & quantity · 2026Article
- Leveraging Administrative Health Databases to Address Health Challenges in Farming Populations: Scoping Review and Bibliometric Analysis (1975-2024).JMIR public health and surveillance · 2025Article
- Food safety practices of individuals before and after the emergence of COVID-19: A pre- and post-comparative analysis.PloS one · 2025Article
- Usability and usefulness of U. S. federal and state public health data dashboards: implications for improving data access and use.Frontiers in public health · 2025Article
- Assessing internal displacement patterns in Ukraine during the beginning of the Russian invasion in 2022.Scientific reports · 2024Article
- Minute level smartphone derived exposure to greenness and consumer wearable derived physical activity in a cohort of US women.Environmental research · 2023Article
- On selection bias in comparison measures of smartphone-generated population mobility: an illustration of no-bias conditions with a commercial data source.Annals of epidemiology · 2022Article
- regentrans: a framework and R package for using genomics to study regional pathogen transmission.Microbial genomics · 2022Article
- From the clinic to the community: Can health system data accurately estimate population obesity prevalence?Obesity (Silver Spring, Md.) · 2021Article
- Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.PloS one · 2021Article
- Impact of Built Environments on Body Weight (the Moving to Health Study): Protocol for a Retrospective Longitudinal Observational Study.JMIR research protocols · 2020Article
- Can Big Data Be Used to Monitor the Mental Health Consequences of COVID-19?International journal of public healthArticle
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
purpose of reviewThe 'big data' revolution affords the opportunity to reuse administrative datasets for public health research. While such datasets offer dramatically increased statistical power compared with conventional primary data collection, typically at much lower cost, their use also raises substantial inferential challenges. In particular, it can be difficult to make population inferences because the sampling frames for many administrative datasets are undefined. We reviewed options for accounting for sampling in big data epidemiology. RECENT
findingsWe identified three common strategies for accounting for sampling when the data available were not collected from a deliberately constructed sample: 1) explicitly reconstruct the sampling frame, 2) test the potential impacts of sampling using sensitivity analyses, and 3) limit inference to sample. SUMMARY: Inference from big data can be challenging because the impacts of sampling are unclear. Attention to sampling frames can minimize risks of bias.
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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.