A partial likelihood approach to smooth estimation of dynamic covariate effects using penalised splines.
Survival data are often modelled by the Cox proportional hazards model, which assumes that covariate effects are constant over time. In recent years however, several new approaches have been suggested which allow covariate effects to vary with time. Non-proportional hazard functions, with covariate effects changing dynamically, can be fitted using penalised spline (P-spline) smoothing. By utilising the link between P-spline smoothing and generalised linear mixed models, the smoothing parameters steering the amount of smoothing can be selected. A hybrid routine, combining the mixed model approach with a classical Akaike criterion, is suggested. This approach is evaluated with simulations and applied to data from the West of Scotland Coronary Prevention Study.
Brown
Denise
D
MRC Social and Public Health Sciences Unit, 4 Lilybank Gardens, University of Glasgow, G12 8RZ, Scotland. denise@msoc.mrc.gla.ac.uk
Kauermann
Göran
G
Ford
Ian
I
D000375:Aging; D003198:Computer Simulation; D003327:Coronary Disease / Q000150:complications / Q000401:mortality; D003627:Data Interpretation, Statistical; D048909:Diabetes Complications / Q000401:mortality; D006801:Humans; D016013:Likelihood Functions; D008875:Middle Aged; D015233:Models, Statistical; D011336:Probability; D016016:Proportional Hazards Models; D012907:Smoking / Q000009:adverse effects