ArticleSpatial and spatio-temporal epidemiology2018
Gaining relevance from the random: Interpreting observed spatial heterogeneity.
Article in Spatial and spatio-temporal epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Understanding the role of environmental and socioeconomic factors in the geographic variation of breast cancer risk in the US-wide Sister Study.Environmental research · 2023Article
- Temporally dependent accelerated failure time model for capturing the impact of events that alter survival in disease mapping.Biostatistics (Oxford, England) · 2019Article
- Trends in Colorectal Cancer Incidence and Survival in Iowa SEER Data: The Timing of It All.Clinical colorectal cancer · 2019Article
- A data-driven approach for estimating the change-points and impact of major events on disease risk.Spatial and spatio-temporal epidemiology · 2019Article
- Assessment of spatial variation in breast cancer-specific mortality using Louisiana SEER data.Social science & medicine (1982) · 2017Article
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
In Bayesian disease mapping, spatial random effects are used to account for confounding in the data so that reasonable estimates for the fixed effects can be obtained. Typically, the spatial random effects are mapped and qualitative comments are made related to an increase or decrease in risk for certain areas. The approach outlined here illustrates how a quantitative secondary assessment can be applied to make more useful and applicable inference related to these spatial random effects. We are able to recover important but unmeasured or unincluded risk factors via a secondary model fit. Results from the secondary model fit can determine association between spatial region-level risk factors and the estimated spatial random effects. We believe this work presents a useful, quantitative technique highlighting the importance and applicability of spatial random effects as well as illustrates how these methods lead to more interpretable conclusions.
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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.