ReviewDer Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete2022
[Methods for analysing spatial data : Using the example of skin cancer prevalence in Germany].
Review in Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 4 citations in OpenAlex.
- [Spatial association between air pollutants and diseases of the respiratory and cardiovascular systems in Hamburg].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Article
- Psoriasis comorbidities in Germany: A population-based study on spatiotemporal variations.PloS one · 2022Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStudies show a spatial variation in skin cancer frequencies, but the causes have not been fully understood. When analysing spatial patterns and correlations, different approaches need to be considered, as the data have special features due to their spatial structure. AIM OF THE STUDY: Why the spatial consideration of skin cancer frequencies is important at all, and which methods of analysis are useful is considered in more detail in this article. The article provides an introduction and overview of statistical methods that are important for the spatial analysis of skin cancer frequencies. MATERIALS AND
methodsAt first it is shown which descriptive methods, such as statistical smoothing, can be applied. Next, spatial cluster and regression analyses will be discussed. Testing for spatial autocorrelation will be considered.
resultsIn particular, the spatial dependence of the data on neighbouring regions, which if ignored can lead to biased estimates, is of particular importance in the analysis of spatial data and requires special spatial analysis methods. DISCUSSION: This article provides an introduction and overview of statistical methods relevant to the spatial analysis of skin cancers.
Indexed as
Identifiers
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.