Variable selection and model choice in structured survival models

Computational Statistics

Volumn 28, Issue 3, 2013, Pages 1079-1101

  Hofner, Benjamin ^a   Hothorn, Torsten ^b   Kneib, Thomas ^c  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

^c UNIVERSITY OF GÖTTINGEN   (Germany)

Author keywords

Hazard regression; Likelihood based boosting; Model choice; P splines; Smooth effects; Time varying effects

Indexed keywords

EID: 84878621391     PISSN: 09434062     EISSN: 16139658     Source Type: Journal    
DOI: 10.1007/s00180-012-0337-x     Document Type: Article

References (36)

1. Abrahamowicz M, MacKenzie TA (2007) Joint estimation of time-dependent and non-linear effects of continuous covariates on survival. Stat Med 26: 392-408.
2. Bender R, Augustin T, Blettner M (2005) Generating survival times to simulate Cox proportional hazards models. Stat Med 24: 1713-1723.
3. Binder H, Schumacher M (2008) Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinform 9: 14.
4. Breiman L (1996) Heuristics of instability and stabilization in model selection. Ann Stat 24: 2350-2383.
5. Bühlmann P, Hothorn T (2007) Boosting algorithms: regularization, prediction and model fitting. Stat Sci 22: 477-505.
6. 2 loss: regression and classification. J Am Stat Assoc 98: 324-339.
7. Cox DR (1972) Regression models and life tables (with discussion). J R Stat Soc Ser B 34: 187-220.
8. de Boor C (1978) A practical guide to splines. Springer, New York.
9. Eilers PHC, Marx BD (1996) Flexible smoothing with B-splines and penalties (with discussion). Stat Sci 11: 89-121.
10. Fahrmeir L, Kneib T, Lang S (2004) Penalized structured additive regression: a Bayesian perspective. Stat Sinica 14: 731-761.
11. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29: 1189-1232.
12. Gray RJ (1992) Flexible methods for analyzing survival data using splines, with application to breast cancer prognosis. J Am Stat Assoc 87: 942-951.
13. Hartl WH, Wolf H, Schneider CP, Küchenhoff H, Jauch KW (2007) Secular trends in mortality associated with new therapeutic strategies in surgical critical illness. Am J Surg 194: 535-541.
14. Hastie T (2007) Comment: Boosting algorithms: regularization, prediction and model fitting. Stat Sci 22: 513-515.
15. Hastie T, Tibshirani R (1993) Varying-coefficient models. J R Stat Soc Ser B 55: 757-796.
16. Hofner B (2009) CoxFlexBoost: Boosting flexible Cox models (with time-varying effects). R package version 0. 7-0, http://R-forge. R-project. org/projects/coxflexboost.
17. Hofner B, Hothorn T, Kneib T, Schmid M (2011a) A framework for unbiased model selection based on boosting. J Comput Graph Stat 20: 956-971.
18. Hofner B, Kneib T, Hartl W, Küchenhoff H (2011b) Building Cox-type structured hazard regression models with time-varying effects. Stat Modell Int J 11: 3-24.
19. Hothorn T, Bühlmann P, Kneib T, Schmid M, Hofner B (2010) Model-based boosting 2. 0. J Mach Learn Res 11: 2109-2113.
20. Hothorn T, Bühlmann P, Kneib T, Schmid M, Hofner B (2012) mboost: Model-Based Boosting. R package version 2. 1-2, http://CRAN. R-project. org/package=mboost.
21. Kneib T, Fahrmeir L (2007) A mixed model approach for geoadditive hazard regression. Scand J Stat 34: 207-228.
22. Kneib T, Hothorn T, Tutz G (2009) Variable selection and model choice in geoadditive regression models. Biometrics 65: 626-634.
23. Mayr A, Hofner B, Schmid M (2012) The importance of knowing when to stop-a sequential stopping rule for component-wise gradient boosting. Methods Inform Med 51: 178-186.
24. Meinshausen N, Bühlmann P (2010) Stability selection (with discussion). J R Stat Soc Ser B 72: 417-473.
25. Moubarak P, Zilker S, Wolf H, Hofner B, Kneib T, Küchenhoff H, Jauch K-W, Hartl WH (2008) Activity-guided antithrombin III therapy in severe surgical sepsis: efficacy and safety according to a retrospective data analysis. Shock 30: 634-641.
26. Müller MH, Moubarak P, Wolf H, Küchenhoff H, Jauch KW, Hartl WH (2008) Independent determinants of early death in critically ill surgical patients. Shock 30: 11-16.
27. Press WH, Teukolsky SA, Vetterling WT, Flannery B (1992) Numerical recipes in C: the art of scientific computing. Cambridge University Press, Cambridge.
28. R Development Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www. R-project. org.
29. Rawlings JO, Pantula S, Dickey DA (1998) Applied regression analysis: a research tool. Springer, New York.
30. Royston P, Altman DG (1994) Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. Appl Stat 43: 429-453.
31. Rüttinger D, Wolf H, Küchenhoff H, Jauch KW, Hartl WH (2007) Red cell transfusion: an essential factor for patient prognosis in surgical critical illness? Shock 28: 165-171.
32. Sauerbrei W, Royston P, Look M (2007) A new proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation. Biometrica J 49: 453-473.
33. Schmid M, Hothorn T (2008) Boosting additive models using component-wise P-splines. Comput Stat Data Anal 53: 298-311.
34. Therneau TM, Grambsch PM (2000) Modeling survival data: extending the Cox model. Springer, New York.
35. Tutz G, Binder H (2006) Generalized additive modelling with implicit variable selection by likelihood-based boosting. Biometrics 62: 961-971.
36. Zucker DM, Karr AF (1990) Non-parametric survival analysis with time-dependent covariate effects: a penalized likelihood approach. Ann Stat 18: 329-352.