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Empirical Study on High-Dimensional Variable Selection and Prediction Under Competing Risks

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New Frontiers of Biostatistics and Bioinformatics

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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Abstract

Competing risk analysis considers event times due to multiple causes, or of more than one event types. Commonly used regression models for such data include (1) cause-specific hazards model, which focuses on modeling one type of event while acknowledging other event types simultaneously; and (2) subdistribution hazards model, which links the covariate effects directly to the cumulative incidence function. Their use and in particular statistical properties in the presence of high-dimensional predictors are largely unexplored. We study the accuracy of prediction and variable selection of existing statistical learning methods under both models using extensive simulation experiments, including different approaches to choosing penalty parameters in each method.

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Author information

Authors and Affiliations

  1. Altman Clinical and Translational Research Institute, University of California, San Diego, La Jolla, CA, USA

    Jiayi Hou

  2. Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA

    Ronghui Xu

  3. Department of Mathematics, University of California, San Diego, La Jolla, CA, USA

    Ronghui Xu

Authors
  1. Jiayi Hou
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  2. Ronghui Xu
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA

    Yichuan Zhao

  2. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Ding-Geng Chen

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Hou, J., Xu, R. (2018). Empirical Study on High-Dimensional Variable Selection and Prediction Under Competing Risks. In: Zhao, Y., Chen, DG. (eds) New Frontiers of Biostatistics and Bioinformatics. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-99389-8_21

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