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Overview of Topics Related to Model Selection for Regression

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Extended Abstracts Fall 2015

Part of the book series: Trends in Mathematics ((RPCRMB,volume 7))

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Abstract

We review some strategies proposed in the literature to combine clinical and omics data in a prediction model. We show how these strategies can be performed by using two well-known statistical methods, lasso and boosting, through an application to a biomedical study with a time-to-event outcome.

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References

  1. V.L. Bauer, H. Braselmann, M. Henke, D. Mattern, A. Walch, K. Unger, M. Baudis, S. Lassmann, R. Huber, J. Wienberg, et al., “Chromosomal changes characterize head and neck cancer with poor prognosis”, Journal of Molecular Medicine 86 (2008), 1353–1365.

    Google Scholar 

  2. H. Binder and M. Schumacher, “Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models”, BMC Bioinformatics 9 (2008), 14.

    Google Scholar 

  3. H. Bøvelstad, S. Nygård, and Ø. Borgan, “Survival prediction from clinico-genomic models —a comparative study”, BMC Bioinformatics 10 (2009), 413.

    Google Scholar 

  4. A.L. Boulesteix and W. Sauerbrei, “Added predictive value of high-throughput molecular data to clinical data and its validation”, Briefings in Bioinformatics 12 (2011), 215–229.

    Google Scholar 

  5. R. De Bin, “Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost”, Computational Statistics (2015).

    Google Scholar 

  6. R. De Bin, W. Sauerbrei, and A.L. Boulesteix, “Investigating the prediction ability of survival models based on both clinical and omics data: two case studies”, Statistics in Medicine 33 (2014), 5310–5329.

    Google Scholar 

  7. J. Friedman, T. Hastie, and R. Tibshirani, “Regularization paths for generalized linear models via coordinate descent”, Journal of Statistical Software 33 (2010), 1.

    Google Scholar 

  8. E. Graf, C. Schmoor, W. Sauerbrei, and M. Schumacher, “Assessment and comparison of prognostic classification schemes for survival data”, Statistics in Medicine  18 (1999), 2529–2545.

    Google Scholar 

  9. H. Seibold, C. Bernau, A.L. Boulesteix, and R. De Bin, “On the choice and influence of the number of boosting steps”, Technical Report 188, Department of Statistics, University of Munich (2016).

    Google Scholar 

  10. E.W. Steyerberg, A.J. Vickers, N.R. Cook, T. Gerds, M. Gonen, N. Obuchowski, M.J. Pencina, and M.W. Kattan, “Assessing the performance of prediction models: a framework for some traditional and novel measures”, Epidemiology 21 (2010), 128.

    Google Scholar 

  11. R. Tibshirani, “Regression shrinkage and selection via the lasso”, Journal of the Royal Statistical Society. Series B (Methodological) 58 (1996), 267–288.

    Google Scholar 

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Acknowledgements

Thanks go to Anne-Laure Boulesteix, Carine Legrand, Herbert Braselmann, Julia Hess and Kristian Unger. RDB was supported by grant BO3139/2-2 from the German Research Foundation (DFG).

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Correspondence to Riccardo De Bin .

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De Bin, R. (2017). Overview of Topics Related to Model Selection for Regression. In: Ainsbury, E., Calle, M., Cardis, E., Einbeck, J., Gómez, G., Puig, P. (eds) Extended Abstracts Fall 2015. Trends in Mathematics(), vol 7. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-55639-0_13

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