Assessing time-by-covariate interactions in proportional hazards regression models using cubic spline functions.
Proportional hazards (or Cox) regression is a popular method for modelling the effects of prognostic factors on survival. Use of cubic spline functions to model time-by-covariate interactions in Cox regression allows investigation of the shape of a possible covariate-time dependence without having to specify a specific functional form. Cubic spline functions allow one to graph such time-by-covariate interactions, to test formally for the proportional hazards assumption, and also to test for non-linearity of the time-by-covariate interaction. The functions can be fitted with existing software using relatively few parameters; the regression coefficients are estimated using standard maximum likelihood methods.
Hess
K R
KR
Department of Patient Studies, University of Texas M.D. Anderson Cancer Center, Houston 77030-4095.
D004812:Epidemiologic Methods; D006801:Humans; D007938:Leukemia / Q000188:drug therapy / Q000401:mortality; D011379:Prognosis; D016016:Proportional Hazards Models; D013274:Stomach Neoplasms / Q000401:mortality / Q000628:therapy; D013997:Time Factors