Abstract
The Principal Component Analysis (PCA) of the tumor fre- quency distribution across Europe highlighted the existence of a strong and reliable deterministic structure organizing the spatial variability of the relative frequency of the different tumors. The analysis highlighted 5 principal “macrocauses” organizing the spatial variability of tumor in- cidences for both sexes. These “macrocauses” were demonstrated to be relatively time invariant and pointed to still undiscovered cancer deter- minants. From a general data analysis point of view, this work proves the ability of PCA of detecting very small signals out of noise and indicates he utility of the technique in epidemiological studies.
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Giuliani, A., Benigni, R. (2000). Principal Component Analysis for Descriptive Epidemiology. In: Brause, R.W., Hanisch, E. (eds) Medical Data Analysis. ISMDA 2000. Lecture Notes in Computer Science, vol 1933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39949-6_37
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DOI: https://doi.org/10.1007/3-540-39949-6_37
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