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Updated: Jun 2, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Multiblock modelling to assess the overall risk factors for a composite outcome
S Bougeard1, C Lupo, S Le Bouquin
1Department of Epidemiology, French Agency for Food, Environmental, and Occupational Health Safety, Zoopole, Ploufragan, France. Stephanie.bougeard@anses.fr
Abstract:
Research in epidemiology may be concerned with assessing risk factors for complex health issues described by several variables. Moreover, epidemiological data are usually organized in several blocks of variables, consisting of a block of variables to be explained and a large number of explanatory variables organized in meaningful blocks. Usual statistical procedures such as generalized linear models do not allow the explanation of a multivariate outcome, such as a complex disease described by several variables, with a single model. Moreover, it is not easy to take account of the organization of explanatory variables into blocks. Here we propose an innovative method in the multiblock modelling framework, called multiblock redundancy analysis, which is designed to handle most specificities of complex epidemiological data. Overall indices and graphical displays associated with different interpretation levels are proposed. The interest and relevance of multiblock redundancy analysis is illustrated using a dataset pertaining to veterinary epidemiology.
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