SynthesisInternational journal of public health2020
A systematic review of Bayesian spatial-temporal models on cancer incidence and mortality.
Synthesis in International journal of public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.
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
9 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A systematic review of spatial and temporal epidemiological approaches, focus on lung cancer risk associated with particulate matter.BMC public health · 2024Pooled it
- Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review.JMIR medical informatics · 2024Pooled it
- A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research.International journal of environmental research and public health · 2023Pooled it
- Bayesian probabilistic projections of proportions with limited data: An application to subnational contraceptive method supply shares.PloS one · 2026Article
- AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis.Discover oncology · 2025Article
- Leveraging spatiotemporal Bayesian analysis to unravel polysubstance use and overdose risk: Opportunities and challenges.Preventive medicine reports · 2025Article
- Syndemic geographic patterns of cancer risk in a health-deprived area of England.Public health in practice (Oxford, England) · 2024Article
- Spatial disparity and factors associated with dementia mortality: A cross-sectional study in Zhejiang Province, China.Frontiers in public health · 2023Article
- Efficacy and safety of acupuncture in patients with cancer-related fatigue: A protocol for systematic review and meta-analysis.Medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to review the types and applications of fully Bayesian (FB) spatial-temporal models and covariates used to study cancer incidence and mortality.
methodsThis systematic review searched articles published within Medline, Embase, Web-of-Science and Google Scholar between 2014 and 2018.
resultsA total of 38 studies were included in our study. All studies applied Bayesian spatial-temporal models to explore spatial patterns over time, and over half assessed the association with risk factors. Studies used different modelling approaches and prior distributions for spatial, temporal and spatial-temporal interaction effects depending on the nature of data, outcomes and applications. The most common Bayesian spatial-temporal model was a generalized linear mixed model. These models adjusted for covariates at the patient, area or temporal level, and through standardization.
conclusionsFew studies (4) modelled patient-level clinical characteristics (11%), and the applications of an FB approach in the forecasting of spatial-temporally aligned cancer data were limited. This review highlighted the need for Bayesian spatial-temporal models to incorporate patient-level prognostic characteristics through the multi-level framework and forecast future cancer incidence and outcomes for cancer prevention and control strategies.
Indexed as
Identifiers
32449006What 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.