Structure estimation of binary graphical models on stratified data: application to the description of injury tables for victims of road accidents

Ballout, Nadim; Viallon, Vivian

doi:10.48550/arxiv.1709.10298

Cited by 1 publication

(2 citation statements)

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“…In particular, it outperforms Ref, which confirms that by by-passing the arbitrary choice of the reference category, data shared lasso generally better accounts for homogeneity than Ref does when such homogeneity exists. These results are consistent with those obtained when evaluating data shared lasso under linear regression models [Ollier and Viallon, 2017] and binary graphical models [Ballout and Viallon, 2017].…”

Section: The Matched Settingsupporting

confidence: 89%

“…However, we shall mention that the group lasso is not well suited when the identification of heterogeneities is of primary interest. On the other hand, the generalized fused lasso has shown good properties in the context of stratified regression models, both un-der generalized linear models [Viallon et al, 2016], survival models [Sennhenn-Reulen and Kneib, 2016] and binary graphical models [Ballout and Viallon, 2017]. Its extension to conditional logistic regression models or multinomial logistic models will be the focus of future work.…”

Section: Discussionmentioning

confidence: 99%

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Sparse estimation for case-control studies with multiple subtypes of cases

Ballout¹,

Garcia²,

Viallon³

2019

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The analysis of case-control studies with several subtypes of cases 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 potential homogeneity among the subtypes of cases, we adapt the ideas of data shared lasso, which has been recently 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 homogeneity that exists among the subtypes of cases: more precisely, when homogeneity 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 potential homogeneity 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 occurrence of subtypes of breast cancer.

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Section: The Matched Settingsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%