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clogitLasso: an R package for high–dimensional analysis of matched case–control and case–crossover data

Abstract : The conditional logistic regression model is the standard tool for the analysis of epidemiological studies in which one or more cases (the event of interest), are individually matched with one or more controls (not showing the event). These situations arise, for example, in matched case-control and case-crossover studies. Usually, regression coefficients are estimated by maximizing the conditional log-likelihood function and variable selection is performed by conventional manual or automatic selection procedures, such as stepwise. These techniques are, however, unsatisfactory in sparse, high-dimensional settings in which penalized methods, such as the lasso (least absolute shrinkage and selection operator) [Tibshirani, 1996], have emerged as an alternative. In particular, the lasso and related methods have recently been adapted to conditional logistic regression [Avalos et al., 2012b]. The R package clogitLasso brings together algorithms to estimate parameters of conditional logistic regression models using lasso, other sparse methods (elastic net, adaptive lasso and bootstrapped versions of lasso). Different criteria are implemented for choosing the regularization term accounting for the dependent nature of data. Resampling methods for evaluating the stability of the selected model are proposed. The most common individually matched study designs are available.
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Submitted on : Monday, August 28, 2017 - 10:24:32 PM
Last modification on : Monday, April 25, 2022 - 2:10:03 PM


  • HAL Id : hal-01578291, version 1



Marta Fernandez Avalos, Yves Grandvalet, Hélène Pouyes, Ludivine Orriols, Emmanuel Lagarde. clogitLasso: an R package for high–dimensional analysis of matched case–control and case–crossover data. CIBB & PRIB 2013, 2013, Nice, France. ⟨hal-01578291⟩



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