Date(s) : 18/11/2019 iCal
14 h 00 min - 16 h 00 min
Vivian VIALLON (Université Pierre Bernard Lyon et IARC)
The analysis of case-control studies with several disease subtypes is increasingly common, e.g. in cancer epidemiology. For matched designs, we show that a natural strategy is based on a stratified conditional logistic regression model. Then, to account for the expected similarities among disease subtypes, we adapt the ideas of data shared lasso, which has recently been proposed for the estimation of regression models in a stratified setting. For unmatched designs, we compare two standard methods based on L1-norm penalized multinomial logistic regression. We describe formal connections between these two approaches, from which practical guidance can be derived. We show that one of these approaches, which is based on a symmetric formulation of the multinomial logistic regression model, actually reduces to a data shared lasso version of the other. Consequently, the relative performance of the two approaches critically depends on the level of similarity that exists among the disease subtypes: more precisely, when similarity is moderate to high, the non-symmetric formulation with controls as the reference is not recommended. Empirical results obtained from synthetic data are presented, which confirm the benefit of properly accounting for similarity under both matched and unmatched designs. We also present preliminary results from the analysis a case-control study nested within the EPIC cohort, where the objective is to identify metabolites associated with the risk of cancer subtypes.
This is a joint work with Nadim Ballout and Cedric Garcia.
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