Building Cox-type structured hazard regression models with time-varying effects

Statistical Modelling

Volumn 11, Issue 1, 2011, Pages 3-24

  Hofner, Benjamin ^a   Kneib, Thomas ^b   Hartl, Wolfgang ^c   Küchenhoff, Helmut ^c  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b UNIVERSITY OF OLDENBURG   (Germany)

^c UNIVERSITY OF MUNICH   (Germany)

Author keywords

hazard regression; mixed models; model building; P splines; prognostic model; time varying effects

Indexed keywords

EID: 78651380685     PISSN: 1471082X     EISSN: 14770342     Source Type: Journal    
DOI: 10.1177/1471082X1001100102     Document Type: Article

References (26)

1. Abrahamowicz M and MacKenzie TA (2007) Joint estimation of time-dependent and non-linear effects of continuous covariates on survival. Statistics in Medicine, 26, 392-408.
2. Bender R, Augustin T and Blettner M (2005) Generating survival times to simulate Cox proportional hazards models. Statistics in Medicine, 24, 1713-23.
3. Cai T, Hyndman R and Wand M (2002) Mixed model-based hazard estimation. Journal of Computational and Graphical Statistics, 11, 784-798.
4. Cox, DR (1972) Regression models and life tables (with discussion). Journal of the Royal Statistical Society, Series B, 34, 187-220.
5. Dierckx P (1993) Curve and surface fitting with splines. Oxford: Clarendon Press.
6. Eilers PHC and Marx BD (1996) Flexible smoothing with B-splines and penalties. Statistical Science, 11, 89-121.
7. Gerds T and Schumacher M (2006) Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biometrical Journal, 48, 1029-40.
8. Gneiting T and Raftery AE (2007) Strictly proper scoring rules prediction, and estimation. Journal of the American Statistical Association, 102, 359-78.
9. Graf E, Schmoor C, Sauerbrei W and Schumacher M (1999) Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine, 18, 2529-45.
10. Gray RJ (1992) Flexible methods for analyzing survival data using splines with application to breast cancer prognosis. Journal of the American Statistical Association. 87, 942-51.
11. Greven S and Kneib T (forthcoming)On the behavior of marginal and conditional Akaike information criteria in linear mixed models. Biometrika. http://biomet.oxfordjournals.org/content/early/2010/07/27/biomet.asg042.abstract.
12. Hastie T and Tibshirani R (1990) Generalized additive models. London: Chapman & Hall/CRC.
13. Hennerfeind A, Brezger A and Fahrmeir L (2006) Geoadditive survival models. Journal of the American Statistical Association, 101, 1065-75.
14. Hurvich C, Simonoff J and Tsai C (1998) Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. Journal of the Royal Statistical Society, Series B, 60, 271-93.
15. Kahn J, Kramer A and Rubenfeld G (2007) Transferring critically ill patients out of hospital improves the standardized mortality ratio: a simulation study. Chest, 131, 68-75.
16. Kauermann G (2005) Penalised spline fitting in multivariable survival models with varying coefficients. Computational Statistics & Data Analysis, 49, 169-86.
17. Kauermann G, Xu R and Vaida F (2008) Stacked Laplace-EM algorithm for duration models with time-varying and random effects. Computational Statistics & Data Analysis, 52, 2514-28.
18. Kneib T and Fahrmeir L (2007) A mixed model approach for geoadditive hazard regression. Scandinavian Journal of Statistics, 34, 207-28.
19. Moubarak P, Zilker S, Wolf H, Hofner B, Kneib T, Küchenhoff H, Jauch KW and Hartl WH (2008)Activity-guided antithrombin III therapy in severe surgical sepsis: efficacy and safety according to a retrospective data analysis. Shock, 30, 634-41.
20. Ruppert D, Wand M and Carroll R (2003) Semiparametric regression. Cambridge: Cambridge University Press.
21. Sauerbrei W and Royston P (1999) Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials. Journal of the Royal Statistical Society, Series A, 162, 71-94.
22. Sauerbrei W, Royston P and Look M (2007) A new proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation. Biometrical Journal, 49, 453-73.
23. Therneau TM and Grambsch PM (2000) Modeling survival data: extending the Cox model. New York: Springer.
24. Vaida F and Blanchard S (2005) Conditional Akaike information for mixed-effects models. Biometrika, 92, 351-70.
25. Wager C, Vaida F and Kauermann G (2007) Model selection for penalized spline smoothing using akaike information criteria. Australian and New Zealand Journal of Statistics, 49, 173-90.
26. Wand MP (2003) Smoothing and mixed models. Computational Statistics, 18, 223-49.