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c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models

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  • Sill, Martin
  • Hielscher, Thomas
  • Becker, Natalia
  • Zucknick, Manuela

Abstract

We have developed the R package c060 with the aim of improving R software func- tionality for high-dimensional risk prediction modeling, e.g., for prognostic modeling of survival data using high-throughput genomic data. Penalized regression models provide a statistically appealing way of building risk prediction models from high-dimensional data. The popular CRAN package glmnet implements an efficient algorithm for fitting penalized Cox and generalized linear models. However, in practical applications the data analysis will typically not stop at the point where the model has been fitted. One is for example often interested in the stability of selected features and in assessing the prediction performance of a model and we provide functions to deal with both of these tasks. Our R functions are computationally efficient and offer the possibility of speeding up computing time through parallel computing. Another feature which can drastically reduce computing time is an efficient interval-search algorithm, which we have implemented for selecting the optimal parameter combination for elastic net penalties. These functions have been useful in our daily work at the Biostatistics department (C060) of the German Cancer Research Center where prognostic modeling of patient survival data is of particular interest. Although we focus on a survival data application of penalized Cox models in this article, the functions in our R package are in general applicable to all types of regression models implemented in the glmnet package, with the exception of prediction error curves, which are specific to time-to-event data.

Suggested Citation

  • Sill, Martin & Hielscher, Thomas & Becker, Natalia & Zucknick, Manuela, 2014. "c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i05).
  • Handle: RePEc:jss:jstsof:v:062:i05
    DOI: http://hdl.handle.net/10.18637/jss.v062.i05
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    References listed on IDEAS

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    1. Mogensen, Ulla B. & Ishwaran, Hemant & Gerds, Thomas A., 2012. "Evaluating Random Forests for Survival Analysis Using Prediction Error Curves," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i11).
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
    4. Binder Harald & Schumacher Martin, 2008. "Adapting Prediction Error Estimates for Biased Complexity Selection in High-Dimensional Bootstrap Samples," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-28, March.
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    Cited by:

    1. Zhi Zhao & Manuela Zucknick, 2020. "Structured penalized regression for drug sensitivity prediction," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 525-545, June.
    2. Mohammad Arashi & Mina Norouzirad & Mahdi Roozbeh & Naushad Mamode Khan, 2021. "A High-Dimensional Counterpart for the Ridge Estimator in Multicollinear Situations," Mathematics, MDPI, vol. 9(23), pages 1-11, November.

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