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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

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2013)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8452))

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 matched with one or more controls (not showing the event). These situations arise, for example, in matched case–control and case–crossover studies. In sparse and high-dimensional settings, penalized methods, such as the Lasso, have emerged as an alternative to conventional estimation and variable selection procedures. We describe the R package clogitLasso, which brings together algorithms to estimate parameters of conditional logistic models using sparsity-inducing penalties. Most individually matched designs are covered, and, beside Lasso, Elastic Net, adaptive Lasso and bootstrapped versions are available. Different criteria for choosing the regularization term are implemented, accounting for the dependency of data. Finally, stability is assessed by resampling methods. We previously review the recent works pertaining to clogitLasso. We also report the use in exploratory analysis of a large pharmacoepidemiological study.

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Authors and Affiliations

  1. INSERM U897-Epidémiologie-Biostatistique, 33000, Bordeaux, France

    Marta Avalos, Hélène Pouyes, Ludivine Orriols & Emmanuel Lagarde

  2. Univ. Bordeaux, 33000, Bordeaux, France

    Marta Avalos, Ludivine Orriols & Emmanuel Lagarde

  3. INRIA SISTM Bordeaux, 33000, Bordeaux, France

    Marta Avalos

  4. CNRS, Heudiasyc UMR6599, Univ. Compiègne, 60200, Compiègne, France

    Yves Grandvalet

  5. Univ. de Pau et des Pays de l’ Adour, 64012, Pau, France

    Hélène Pouyes

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  1. Marta Avalos
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  2. Yves Grandvalet
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  3. Hélène Pouyes
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  4. Ludivine Orriols
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  5. Emmanuel Lagarde
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Correspondence to Marta Avalos .

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Editors and Affiliations

  1. University Nice Sophia Antipolis, Sophia Antipolis, France

    Enrico Formenti

  2. University of Salerno, Fisciano, Italy

    Roberto Tagliaferri

  3. University of Groningen, AG Groningen, The Netherlands

    Ernst Wit

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Avalos, M., Grandvalet, Y., Pouyes, H., Orriols, L., Lagarde, E. (2014). 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. In: Formenti, E., Tagliaferri, R., Wit, E. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2013. Lecture Notes in Computer Science(), vol 8452. Springer, Cham. https://doi.org/10.1007/978-3-319-09042-9_8

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