High–Dimensional Sparse Matched Case–Control and Case–Crossover Data: A Review of Recent Works, Description of an R Tool and an Illustration of the Use in Epidemiological Studies

Avalos, Marta; Grandvalet, Yves; Pouyes, Hélène; Orriols, Ludivine; Lagarde, Emmanuel

doi:10.1007/978-3-319-09042-9_8

Cited by 2 publications

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“…After this discretization, the optimal regularization parameter can be chosen by a model selection criterion such as cross-validation or the Bayesian Information Criterion (BIC) [ 43 , 44 ].…”

Section: The Regularization Pathmentioning

confidence: 99%

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Sparse conditional logistic regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm

et al. 2015

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This paper considers the problem of estimation and variable selection for large high-dimensional data (high number of predictors p and large sample size N, without excluding the possibility that N < p) resulting from an individually matched case-control study. We develop a simple algorithm for the adaptation of the Lasso and related methods to the conditional logistic regression model. Our proposal relies on the simplification of the calculations involved in the likelihood function. Then, the proposed algorithm iteratively solves reweighted Lasso problems using cyclical coordinate descent, computed along a regularization path. This method can handle large problems and deal with sparse features efficiently. We discuss benefits and drawbacks with respect to the existing available implementations. We also illustrate the interest and use of these techniques on a pharmacoepidemiological study of medication use and traffic safety.

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Section: The Regularization Pathmentioning

confidence: 99%

“…Other practical options are available, for example penalized and unpenalized (always included in the model) variables can be specified. The methods, model selection criteria and capabilities of clogitLasso are detailed in [ 51 , 44 ].…”

Section: The Regularization Pathmentioning

confidence: 99%

Sparse conditional logistic regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm

et al. 2015

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The case-crossover design via penalized regression

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Mockenhaupt

Naldi

et al. 2016

BMC Med Res Methodol

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BackgroundThe case-crossover design is an attractive alternative to the classical case–control design which can be used to study the onset of acute events if the risk factors of interest vary in time. By comparing exposures within cases at different time periods, the case-crossover design does not rely on control subjects which can be difficult to acquire. However, using the standard method of maximum likelihood, resulting risk estimates can be heavily biased when the prevalence to risk factors is very low (or very high).MethodsTo overcome the problem of low risk factor prevalences, penalized conditional logistic regression via the lasso (least absolute shrinkage and selection operator) has been proposed in the literature as well as related methods such as the Firth correction. We apply and compare several penalized regression approaches in the context of a case-crossover analysis of the European Study of Severe Cutaneous Adverse Reactions (EuroSCAR; 1997–2001).ResultsOut of 30 drugs, standard methods only correctly classified 17 drugs (including some highly implausible risk estimates), while penalized methods correctly classified 22 drugs.ConclusionPenalized methods generally yield better risk classifications and much more plausible risk estimates for the EuroSCAR study than standard methods. As these novel techniques can be easily implemented using available R packages, we encourage routine use of penalized conditional logistic regression for case-crossover data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0197-0) contains supplementary material, which is available to authorized users.

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High–Dimensional Sparse Matched Case–Control and Case–Crossover Data: A Review of Recent Works, Description of an R Tool and an Illustration of the Use in Epidemiological Studies

Cited by 2 publications

References 68 publications

Sparse conditional logistic regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm

Sparse conditional logistic regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm

The case-crossover design via penalized regression

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