Comparing smoothing techniques for fitting the nonlinear effect of covariate in cox models

Acta Informatica Medica

Volumn 24, Issue 1, 2016, Pages 38-41

  Roshani, Daem ^a   Ghaderi, E ^a  

^a KURDISTAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

Author keywords

Cox model; Natural spline; Penalized spline; Restricted cubic spline; Smoothing

Indexed keywords

COMPUTER PROGRAM; DATA ANALYSIS; GENETIC POLYMORPHISM; PROPORTIONAL HAZARDS MODEL; SAMPLE SIZE; STATISTICAL MODEL;

EID: 84957074051     PISSN: 03538109     EISSN: 19865988     Source Type: Journal    
DOI: 10.5455/aim.2016.24.38-41     Document Type: Article

References (16)

1. Sleeper LA, Harrington DP. Regression splines in the Cox model with application to covariate effects in liver disease. Journal of the American Statistical Association. 1990; 85 (412): 941-9.
2. Keele LJ. Semiparametric regression for the social sciences: John Wiley & Sons; 2008.
3. Cao Y, Lin H, Wu TZ, Yu Y. Penalized spline estimation for functional coefficient regression models. Computational statistics & data analysis. 2010; 54 (4): 891-905.
4. LeBlanc M, Crowley J. Adaptive regression splines in the Cox model. Biometrics. 1999; 55 (1): 204-13.
5. Tibshirani R, Hastie T. Local likelihood estimation. Journal of the American Statistical Association. 1987; 82 (398): 559-67.
6. Gurrin LC, Scurrah KJ, Hazelton ML. Tutorial in biostatistics: spline smoothing with linear mixed models. Statistics in medicine. 2005; 24(21): 3361.
7. Gray RJ. Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. Journal of the American Statistical Association. 1992; 87(420): 942-51.
8. Eisen E, Agalliu I, Thurston S, Coull B, Checkoway H. Smoothing in occupational cohort studies: an illustration based on penalised splines. Occupational and environmental medicine. 2004; 61(10): 854-60.
9. Thurston SW, Eisen EA, Schwartz J. Smoothing in survival models: an application to workers exposed to metalworking fluids. Epidemiology. 2002; 13(6): 685-92.
10. Ruppert D. Selecting the number of knots for penalized splines. Journal of computational and graphical statistics. 2012.
11. Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Statistics in medicine. 2005; 24(11): 1713-23.
12. Strasak AM, Umlauf N, Pfeiffer RM, Lang S. Comparing penalized splines and fractional polynomials for flexible modelling of the effects of continuous predictor variables. Computational statistics & data analysis. 2011; 55(4): 1540-51.
13. Esmail Nasab N, Roshani D, Azadi N. Use of Single Index Model for estimation of survival of the patients with acute myocardial infarction. Scientific Journal of Kurdistan University of Medical Sciences. 2012; 17(3): 102-9.
14. Malloy EJ, Spiegelman D, Eisen EA. Comparing measures of model selection for penalized splines in Cox models. Computational statistics & data analysis. 2009; 53(7): 2605-16.
15. Eilers PH, Marx BD. Splines, knots, and penalties. Wiley Interdisciplinary Reviews: Computational Statistics. 2010; 2 (6): 637-53.
16. Yao F, Lee TC. On knot placement for penalized spline regression. Journal of the Korean Statistical Society. 2008; 37 (3): 259-67.