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Linear regression modeling of interval-censored survival times based on a convex piecewise-linear criterion function

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Języki publikacji
EN
Abstrakty
EN
Regression models of censored survival data are often required to handle the cases, where information on the dependent (response) variable is only available as intervals, within which the actual values are located. We report on implementation and some preliminary tests of a new general method for regression with an interval-censored response variable. This method is based on minimization of a convex piecewise-linear (CPL) criterion function introduced earlier for perceptron-type classifier design. The presented interval regression method (CPL- IR) can handle arbitrary pattern of exact and left-, right-, or interval-censored data in one flexible computational framework.
Twórcy
autor
autor
  • Nałęcz Institute of Biocybernetics and Biomedical Engineering, Polish Academy Sciences, ul. Ks. Trojdena 4, 02-109 Warsaw, Poland, pkaluzny@ibib.waw.pl
Bibliografia
  • [1] Klein J. P., Moeschberger M. L.: Survival analysis: techniques for censored and truncated data. Springer, 1997.
  • [2] Allison P. D.: Survival Analysis using the SAS System. A Practical Guide, SAS Institute, 1995.
  • [3] Therneau T.: A Package for Survival Analysis in S, R package “survival” v. 2.36-5, http://cran.rproject.org/package=survival, 2011.
  • [4] Lesaffre E., Komarek A., Declerk D.: An overview of methods for interval-censored data with an emphasis on applications in dentistry. Statistical Methods in Medical Research 2005, 14, 539–552.
  • [5] Bobrowski L.: Linear prognostic models based on interval regression with CPL functions (in Polish). Symulacja w Badaniach i Rozwoju 2010, 1, 109–117.
  • [6] Bobrowski L.: Prognostic Models Based on Linear Separability. In: P. Perner (Ed.) Advances in Data Mining, Springer Verlag, Berlin 2011, 11–24.
  • [7] R Development Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, http://www.r-project.org, 2010.
  • [8] Bobrowski L., Niemiro W.: A method of synthesis of linear discriminant function in the case of nonseparability. Pattern Recognition 1984, 17, 177–273.
  • [9] Bobrowski L.: Design of piecewise linear classifiers from formal neurons by some basis exchange technique. Pattern Recognition, 1991, 24, 863–870.
  • [10] Bobrowski L.: Interval Uncertainty in CPL Models for Computer Aided Prognosis, In: Z. Hippe, J.L. Kulikowski, T. Mroczek (Eds) Human-Computer Systems Interaction. Backgrounds and Applications 2, Springer, Berlin 2012, 443–461.
  • [11] Henschel V., Heiss C., Mansmann U.: intcox: Iterated Convex Minorant Algorithm for interval censored event data, R package v. 0.9.2, http://cran.r-project.org/package=intcox, 2009.
  • [12] Pan W.: Extending the Iterative Convex Minorant Algorithm to the Cox Model for Interval-Censored Data. Journal of Computational and Graphical Statistics 1999, 78, 109–120.
  • [13] Henschel V., Heiss C., Mansmann U.: Intcox: Compendium to apply the iterative convex minorant algorithm to interval censored event data, http://cran.r-project.org/web/packages/intcox/vignettes/intcox.pdf, 2009.
  • [14] Buckley, J., James, I.: Linear regression with censored data. Biometrika 1979, 66, 429–436.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ6-0002-0028
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