ArticleCancer control : journal of the Moffitt Cancer Center
On Cluster Structures of Finnish Cancer Incidence Data.
Article in Cancer control : journal of the Moffitt Cancer Center. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
IntroductionThe global burden of cancer is increasing. Part of this development is attributable to the estimated growth and aging of the population. In particular, aging is 1 of the main risk factors for cancer. However, there are many other risk factors beyond aging, including certain lifestyle and environmental factors. In addition, changes in diagnostic thresholds, increasing coverage of screening, and other similar factors affect cancer incidence rates. Therefore, even after excluding the effect of aging of the population, cancer incidence rates have not remained constant over time. To study these changes, the focus of this study is to identify and analyze cluster structures of the Finnish cancer incidence data from 1963 to 2023.MethodsTo uncover the cluster structures, a proximity measure that is based on the shape of the curves is used. For unstandardized data, the proximity measure is shown to be invariant under simple location shift, and for standardized data, also under simple scaling, making the proximity measure suitable for assessing the similarities or dissimilarities of trends in time. As the group-building algorithm, agglomerative hierarchical clustering, combined with the average linkage method, is used.ResultsThe cluster structures were identified for 12 different subgroups, determined by age and sex. In many cases, cancers for which there exists a national screening program, including breast and cervical cancer, or an individualized testing tool, including prostate cancer, formed clusters of their own. Melanoma of the skin and lung & tracheal cancer are other 2 cancer types that often separated as their own clusters, possibly due to certain lifestyle factors.ConclusionThe study demonstrates the potential of the proposed proximity in the given context. In addition, the analysis of the cluster structures provides some insight into the Finnish cancer epidemiology.
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.