A methodological framework for occupational cancer surveillance: Addressing multiplicity and unmeasured confounding in Italian registry data

Objectives: The Big Data and Deep Learning in the surveillance of occupational cancers (BEST) project aimed to enhance occupational cancer surveillance in Italy by using large-scale registry linkages. It addressed two methodological challenges: controlling for multiple testing/selective inference wh...

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Autors principals:	Mirko Berti, Giorgia Stoppa, Claudio Gariazzo, Dario Consonni, Stefania Massari, Annibale Biggeri, Dolores Catelan
Format:	Artigo
Idioma:	Inglês
Publicat:	Elsevier 2026-06-01
Col·lecció:	Hygiene and Environmental Health Advances
Matèries:	Occupational cancer surveillance Multiple testing Unmeasured confounding E-values League tables
Accés en línia:	http://www.sciencedirect.com/science/article/pii/S2773049226000127
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